Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations725
Missing cells16110
Missing cells (%)46.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory272.0 KiB
Average record size in memory384.2 B

Variable types

Numeric10
Text19
Categorical9
Unsupported7
DateTime3

Alerts

averageRuntime is highly overall correlated with runtimeHigh correlation
externals.thetvdb is highly overall correlated with externals.tvrage and 1 other fieldsHigh correlation
externals.tvrage is highly overall correlated with externals.thetvdb and 4 other fieldsHigh correlation
id is highly overall correlated with externals.thetvdb and 1 other fieldsHigh correlation
language is highly overall correlated with network.country.code and 5 other fieldsHigh correlation
network.country.code is highly overall correlated with externals.tvrage and 7 other fieldsHigh correlation
network.country.name is highly overall correlated with externals.tvrage and 7 other fieldsHigh correlation
network.country.timezone is highly overall correlated with externals.tvrage and 7 other fieldsHigh correlation
network.id is highly overall correlated with externals.tvrage and 1 other fieldsHigh correlation
rating.average is highly overall correlated with network.idHigh correlation
runtime is highly overall correlated with averageRuntimeHigh correlation
status is highly overall correlated with network.country.code and 2 other fieldsHigh correlation
webChannel.country.code is highly overall correlated with language and 6 other fieldsHigh correlation
webChannel.country.name is highly overall correlated with language and 6 other fieldsHigh correlation
webChannel.country.timezone is highly overall correlated with language and 6 other fieldsHigh correlation
webChannel.id is highly overall correlated with webChannel.country.code and 2 other fieldsHigh correlation
weight is highly overall correlated with idHigh correlation
language has 47 (6.5%) missing values Missing
runtime has 582 (80.3%) missing values Missing
averageRuntime has 62 (8.6%) missing values Missing
ended has 542 (74.8%) missing values Missing
officialSite has 82 (11.3%) missing values Missing
network has 725 (100.0%) missing values Missing
dvdCountry has 725 (100.0%) missing values Missing
summary has 97 (13.4%) missing values Missing
schedule.time has 497 (68.6%) missing values Missing
rating.average has 597 (82.3%) missing values Missing
webChannel.id has 22 (3.0%) missing values Missing
webChannel.name has 22 (3.0%) missing values Missing
webChannel.country.name has 292 (40.3%) missing values Missing
webChannel.country.code has 292 (40.3%) missing values Missing
webChannel.country.timezone has 292 (40.3%) missing values Missing
webChannel.officialSite has 180 (24.8%) missing values Missing
externals.tvrage has 702 (96.8%) missing values Missing
externals.thetvdb has 204 (28.1%) missing values Missing
externals.imdb has 353 (48.7%) missing values Missing
image.medium has 34 (4.7%) missing values Missing
image.original has 34 (4.7%) missing values Missing
image has 725 (100.0%) missing values Missing
_links.nextepisode.href has 661 (91.2%) missing values Missing
_links.nextepisode.name has 661 (91.2%) missing values Missing
network.id has 672 (92.7%) missing values Missing
network.name has 672 (92.7%) missing values Missing
network.country.name has 672 (92.7%) missing values Missing
network.country.code has 672 (92.7%) missing values Missing
network.country.timezone has 672 (92.7%) missing values Missing
network.officialSite has 707 (97.5%) missing values Missing
webChannel has 725 (100.0%) missing values Missing
webChannel.country has 725 (100.0%) missing values Missing
dvdCountry.name has 721 (99.4%) missing values Missing
dvdCountry.code has 721 (99.4%) missing values Missing
dvdCountry.timezone has 721 (99.4%) missing values Missing
id has unique values Unique
url has unique values Unique
updated has unique values Unique
_links.self.href has unique values Unique
_links.previousepisode.href has unique values Unique
genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
network is an unsupported type, check if it needs cleaning or further analysis Unsupported
dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
image is an unsupported type, check if it needs cleaning or further analysis Unsupported
webChannel is an unsupported type, check if it needs cleaning or further analysis Unsupported
webChannel.country is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight has 13 (1.8%) zeros Zeros

Reproduction

Analysis started2025-03-30 14:27:01.291690
Analysis finished2025-03-30 14:27:18.912056
Duration17.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61766.754
Minimum274
Maximum83688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:18.999348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile12071.8
Q155087
median69798
Q373949
95-th percentile80564.8
Maximum83688
Range83414
Interquartile range (IQR)18862

Descriptive statistics

Standard deviation19436.276
Coefficient of variation (CV)0.31467213
Kurtosis2.0175264
Mean61766.754
Median Absolute Deviation (MAD)6264
Skewness-1.6392748
Sum44780897
Variance3.7776883 × 108
MonotonicityNot monotonic
2025-03-30T09:27:19.115632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75220 1
 
0.1%
51908 1
 
0.1%
59205 1
 
0.1%
59484 1
 
0.1%
72871 1
 
0.1%
53123 1
 
0.1%
82955 1
 
0.1%
74384 1
 
0.1%
73901 1
 
0.1%
50644 1
 
0.1%
Other values (715) 715
98.6%
ValueCountFrequency (%)
274 1
0.1%
703 1
0.1%
718 1
0.1%
729 1
0.1%
793 1
0.1%
802 1
0.1%
812 1
0.1%
875 1
0.1%
920 1
0.1%
938 1
0.1%
ValueCountFrequency (%)
83688 1
0.1%
83666 1
0.1%
83588 1
0.1%
83539 1
0.1%
83496 1
0.1%
83491 1
0.1%
83471 1
0.1%
83243 1
0.1%
83242 1
0.1%
83241 1
0.1%

url
Text

Unique 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:19.326892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length97
Median length74
Mean length51.873103
Min length35

Characters and Unicode

Total characters37608
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique725 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/shows/51908/neznost
2nd rowhttps://www.tvmaze.com/shows/59205/predposlednaa-instancia
3rd rowhttps://www.tvmaze.com/shows/59484/manuna
4th rowhttps://www.tvmaze.com/shows/72871/nedetskoe-kino
5th rowhttps://www.tvmaze.com/shows/73221/uspesnyj
ValueCountFrequency (%)
https://www.tvmaze.com/shows/73590/kak-druza-zahara-zenili 1
 
0.1%
https://www.tvmaze.com/shows/75220/sues-places 1
 
0.1%
https://www.tvmaze.com/shows/51908/neznost 1
 
0.1%
https://www.tvmaze.com/shows/59205/predposlednaa-instancia 1
 
0.1%
https://www.tvmaze.com/shows/59484/manuna 1
 
0.1%
https://www.tvmaze.com/shows/71522/tiktok-murder-gone-viral 1
 
0.1%
https://www.tvmaze.com/shows/74407/for-the-culture-with-amanda-parris 1
 
0.1%
https://www.tvmaze.com/shows/47865/a-seba-znau 1
 
0.1%
https://www.tvmaze.com/shows/59613/frendzona 1
 
0.1%
https://www.tvmaze.com/shows/74261/broken-the-heart 1
 
0.1%
Other values (715) 715
98.6%
2025-03-30T09:27:19.658271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 3625
 
9.6%
w 3082
 
8.2%
t 2925
 
7.8%
s 2904
 
7.7%
o 2250
 
6.0%
e 1949
 
5.2%
h 1899
 
5.0%
m 1775
 
4.7%
a 1688
 
4.5%
. 1450
 
3.9%
Other values (30) 14061
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26740
71.1%
Other Punctuation 5800
 
15.4%
Decimal Number 3628
 
9.6%
Dash Punctuation 1440
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 3082
11.5%
t 2925
10.9%
s 2904
10.9%
o 2250
 
8.4%
e 1949
 
7.3%
h 1899
 
7.1%
m 1775
 
6.6%
a 1688
 
6.3%
c 993
 
3.7%
p 961
 
3.6%
Other values (16) 6314
23.6%
Decimal Number
ValueCountFrequency (%)
7 594
16.4%
6 448
12.3%
4 404
11.1%
3 380
10.5%
5 331
9.1%
2 314
8.7%
8 311
8.6%
1 300
8.3%
9 274
7.6%
0 272
7.5%
Other Punctuation
ValueCountFrequency (%)
/ 3625
62.5%
. 1450
 
25.0%
: 725
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26740
71.1%
Common 10868
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 3082
11.5%
t 2925
10.9%
s 2904
10.9%
o 2250
 
8.4%
e 1949
 
7.3%
h 1899
 
7.1%
m 1775
 
6.6%
a 1688
 
6.3%
c 993
 
3.7%
p 961
 
3.6%
Other values (16) 6314
23.6%
Common
ValueCountFrequency (%)
/ 3625
33.4%
. 1450
 
13.3%
- 1440
 
13.2%
: 725
 
6.7%
7 594
 
5.5%
6 448
 
4.1%
4 404
 
3.7%
3 380
 
3.5%
5 331
 
3.0%
2 314
 
2.9%
Other values (4) 1157
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 3625
 
9.6%
w 3082
 
8.2%
t 2925
 
7.8%
s 2904
 
7.7%
o 2250
 
6.0%
e 1949
 
5.2%
h 1899
 
5.0%
m 1775
 
4.7%
a 1688
 
4.5%
. 1450
 
3.9%
Other values (30) 14061
37.4%

name
Text

Distinct723
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:19.939171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length63
Median length40
Mean length17.206897
Min length2

Characters and Unicode

Total characters12475
Distinct characters170
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique721 ?
Unique (%)99.4%

Sample

1st rowНежность
2nd rowПредпоследняя инстанция
3rd rowМанюня
4th rowНедетское кино
5th rowУспешный
ValueCountFrequency (%)
the 112
 
5.2%
of 39
 
1.8%
with 21
 
1.0%
a 20
 
0.9%
17
 
0.8%
love 17
 
0.8%
and 15
 
0.7%
in 12
 
0.6%
no 12
 
0.6%
you 12
 
0.6%
Other values (1461) 1897
87.3%
2025-03-30T09:27:20.348113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1449
 
11.6%
e 1088
 
8.7%
a 739
 
5.9%
n 645
 
5.2%
o 644
 
5.2%
i 624
 
5.0%
r 576
 
4.6%
t 511
 
4.1%
s 467
 
3.7%
h 357
 
2.9%
Other values (160) 5375
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8750
70.1%
Uppercase Letter 2049
 
16.4%
Space Separator 1449
 
11.6%
Other Punctuation 158
 
1.3%
Decimal Number 43
 
0.3%
Dash Punctuation 22
 
0.2%
Other Symbol 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1088
 
12.4%
a 739
 
8.4%
n 645
 
7.4%
o 644
 
7.4%
i 624
 
7.1%
r 576
 
6.6%
t 511
 
5.8%
s 467
 
5.3%
h 357
 
4.1%
l 355
 
4.1%
Other values (76) 2744
31.4%
Uppercase Letter
ValueCountFrequency (%)
S 192
 
9.4%
T 184
 
9.0%
M 115
 
5.6%
L 113
 
5.5%
C 101
 
4.9%
A 100
 
4.9%
D 99
 
4.8%
B 92
 
4.5%
W 89
 
4.3%
H 88
 
4.3%
Other values (47) 876
42.8%
Other Punctuation
ValueCountFrequency (%)
: 62
39.2%
' 43
27.2%
. 14
 
8.9%
, 11
 
7.0%
! 10
 
6.3%
& 9
 
5.7%
? 5
 
3.2%
/ 1
 
0.6%
* 1
 
0.6%
… 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 9
20.9%
2 9
20.9%
0 8
18.6%
3 4
9.3%
4 4
9.3%
7 3
 
7.0%
9 2
 
4.7%
5 2
 
4.7%
6 1
 
2.3%
8 1
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 19
86.4%
– 3
 
13.6%
Other Symbol
ValueCountFrequency (%)
● 2
66.7%
° 1
33.3%
Space Separator
ValueCountFrequency (%)
1449
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9837
78.9%
Common 1676
 
13.4%
Cyrillic 955
 
7.7%
Greek 7
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1088
 
11.1%
a 739
 
7.5%
n 645
 
6.6%
o 644
 
6.5%
i 624
 
6.3%
r 576
 
5.9%
t 511
 
5.2%
s 467
 
4.7%
h 357
 
3.6%
l 355
 
3.6%
Other values (69) 3831
38.9%
Cyrillic
ValueCountFrequency (%)
о 78
 
8.2%
а 75
 
7.9%
е 69
 
7.2%
и 61
 
6.4%
н 57
 
6.0%
р 56
 
5.9%
т 49
 
5.1%
с 38
 
4.0%
к 35
 
3.7%
л 33
 
3.5%
Other values (47) 404
42.3%
Common
ValueCountFrequency (%)
1449
86.5%
: 62
 
3.7%
' 43
 
2.6%
- 19
 
1.1%
. 14
 
0.8%
, 11
 
0.7%
! 10
 
0.6%
1 9
 
0.5%
& 9
 
0.5%
2 9
 
0.5%
Other values (17) 41
 
2.4%
Greek
ValueCountFrequency (%)
Κ 1
14.3%
ω 1
14.3%
σ 1
14.3%
τ 1
14.3%
λ 1
14.3%
έ 1
14.3%
ς 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11445
91.7%
Cyrillic 955
 
7.7%
None 69
 
0.6%
Punctuation 4
 
< 0.1%
Geometric Shapes 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1449
 
12.7%
e 1088
 
9.5%
a 739
 
6.5%
n 645
 
5.6%
o 644
 
5.6%
i 624
 
5.5%
r 576
 
5.0%
t 511
 
4.5%
s 467
 
4.1%
h 357
 
3.1%
Other values (65) 4345
38.0%
Cyrillic
ValueCountFrequency (%)
о 78
 
8.2%
а 75
 
7.9%
е 69
 
7.2%
и 61
 
6.4%
н 57
 
6.0%
р 56
 
5.9%
т 49
 
5.1%
с 38
 
4.0%
к 35
 
3.7%
л 33
 
3.5%
Other values (47) 404
42.3%
None
ValueCountFrequency (%)
å 9
 
13.0%
ü 5
 
7.2%
ä 5
 
7.2%
ö 5
 
7.2%
é 4
 
5.8%
â 3
 
4.3%
ş 3
 
4.3%
ñ 3
 
4.3%
ı 2
 
2.9%
ø 2
 
2.9%
Other values (25) 28
40.6%
Punctuation
ValueCountFrequency (%)
– 3
75.0%
… 1
 
25.0%
Geometric Shapes
ValueCountFrequency (%)
● 2
100.0%

type
Categorical

Distinct11
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Scripted
235 
Animation
117 
Documentary
95 
Reality
91 
Talk Show
82 
Other values (6)
105 

Length

Max length11
Median length10
Mean length8.3268966
Min length4

Characters and Unicode

Total characters6037
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowScripted
2nd rowScripted
3rd rowScripted
4th rowScripted
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted 235
32.4%
Animation 117
16.1%
Documentary 95
13.1%
Reality 91
 
12.6%
Talk Show 82
 
11.3%
News 39
 
5.4%
Game Show 32
 
4.4%
Variety 16
 
2.2%
Sports 13
 
1.8%
Panel Show 4
 
0.6%

Length

2025-03-30T09:27:20.446922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scripted 235
27.8%
show 119
14.1%
animation 117
13.9%
documentary 95
11.3%
reality 91
 
10.8%
talk 82
 
9.7%
news 39
 
4.6%
game 32
 
3.8%
variety 16
 
1.9%
sports 13
 
1.5%
Other values (2) 5
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 576
 
9.5%
t 567
 
9.4%
e 512
 
8.5%
a 438
 
7.3%
S 367
 
6.1%
r 360
 
6.0%
o 344
 
5.7%
n 333
 
5.5%
c 330
 
5.5%
p 248
 
4.1%
Other values (18) 1962
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5074
84.0%
Uppercase Letter 844
 
14.0%
Space Separator 119
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 576
11.4%
t 567
11.2%
e 512
10.1%
a 438
 
8.6%
r 360
 
7.1%
o 344
 
6.8%
n 333
 
6.6%
c 330
 
6.5%
p 248
 
4.9%
m 244
 
4.8%
Other values (8) 1122
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 367
43.5%
A 118
 
14.0%
D 95
 
11.3%
R 91
 
10.8%
T 82
 
9.7%
N 39
 
4.6%
G 32
 
3.8%
V 16
 
1.9%
P 4
 
0.5%
Space Separator
ValueCountFrequency (%)
119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5918
98.0%
Common 119
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 576
 
9.7%
t 567
 
9.6%
e 512
 
8.7%
a 438
 
7.4%
S 367
 
6.2%
r 360
 
6.1%
o 344
 
5.8%
n 333
 
5.6%
c 330
 
5.6%
p 248
 
4.2%
Other values (17) 1843
31.1%
Common
ValueCountFrequency (%)
119
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 576
 
9.5%
t 567
 
9.4%
e 512
 
8.5%
a 438
 
7.3%
S 367
 
6.1%
r 360
 
6.0%
o 344
 
5.7%
n 333
 
5.5%
c 330
 
5.5%
p 248
 
4.1%
Other values (18) 1962
32.5%

language
Categorical

High correlation  Missing 

Distinct33
Distinct (%)4.9%
Missing47
Missing (%)6.5%
Memory size5.8 KiB
English
280 
Chinese
117 
Russian
66 
Norwegian
31 
Korean
 
23
Other values (28)
161 

Length

Max length10
Median length7
Mean length6.9646018
Min length4

Characters and Unicode

Total characters4722
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.5%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 280
38.6%
Chinese 117
16.1%
Russian 66
 
9.1%
Norwegian 31
 
4.3%
Korean 23
 
3.2%
Swedish 23
 
3.2%
Japanese 15
 
2.1%
Spanish 12
 
1.7%
French 11
 
1.5%
Thai 11
 
1.5%
Other values (23) 89
 
12.3%
(Missing) 47
 
6.5%

Length

2025-03-30T09:27:20.546825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 280
41.3%
chinese 117
17.3%
russian 66
 
9.7%
norwegian 31
 
4.6%
korean 23
 
3.4%
swedish 23
 
3.4%
japanese 15
 
2.2%
spanish 12
 
1.8%
french 11
 
1.6%
thai 11
 
1.6%
Other values (23) 89
 
13.1%

Most occurring characters

ValueCountFrequency (%)
i 619
13.1%
n 618
13.1%
s 608
12.9%
h 492
10.4%
e 380
8.0%
g 317
6.7%
l 293
 
6.2%
E 280
 
5.9%
a 240
 
5.1%
C 124
 
2.6%
Other values (32) 751
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4044
85.6%
Uppercase Letter 678
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 619
15.3%
n 618
15.3%
s 608
15.0%
h 492
12.2%
e 380
9.4%
g 317
7.8%
l 293
7.2%
a 240
 
5.9%
r 107
 
2.6%
u 92
 
2.3%
Other values (13) 278
6.9%
Uppercase Letter
ValueCountFrequency (%)
E 280
41.3%
C 124
18.3%
R 66
 
9.7%
S 36
 
5.3%
N 31
 
4.6%
K 23
 
3.4%
T 22
 
3.2%
D 15
 
2.2%
J 15
 
2.2%
F 14
 
2.1%
Other values (9) 52
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4722
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 619
13.1%
n 618
13.1%
s 608
12.9%
h 492
10.4%
e 380
8.0%
g 317
6.7%
l 293
 
6.2%
E 280
 
5.9%
a 240
 
5.1%
C 124
 
2.6%
Other values (32) 751
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 619
13.1%
n 618
13.1%
s 608
12.9%
h 492
10.4%
e 380
8.0%
g 317
6.7%
l 293
 
6.2%
E 280
 
5.9%
a 240
 
5.1%
C 124
 
2.6%
Other values (32) 751
15.9%

genres
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.8 KiB

status
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Running
446 
Ended
183 
To Be Determined
96 

Length

Max length16
Median length7
Mean length7.6868966
Min length5

Characters and Unicode

Total characters5573
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running 446
61.5%
Ended 183
25.2%
To Be Determined 96
 
13.2%

Length

2025-03-30T09:27:20.643679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T09:27:20.715577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
running 446
48.6%
ended 183
20.0%
to 96
 
10.5%
be 96
 
10.5%
determined 96
 
10.5%

Most occurring characters

ValueCountFrequency (%)
n 1617
29.0%
e 567
 
10.2%
i 542
 
9.7%
d 462
 
8.3%
R 446
 
8.0%
u 446
 
8.0%
g 446
 
8.0%
192
 
3.4%
E 183
 
3.3%
T 96
 
1.7%
Other values (6) 576
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4464
80.1%
Uppercase Letter 917
 
16.5%
Space Separator 192
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1617
36.2%
e 567
 
12.7%
i 542
 
12.1%
d 462
 
10.3%
u 446
 
10.0%
g 446
 
10.0%
o 96
 
2.2%
t 96
 
2.2%
r 96
 
2.2%
m 96
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
R 446
48.6%
E 183
20.0%
T 96
 
10.5%
B 96
 
10.5%
D 96
 
10.5%
Space Separator
ValueCountFrequency (%)
192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5381
96.6%
Common 192
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1617
30.1%
e 567
 
10.5%
i 542
 
10.1%
d 462
 
8.6%
R 446
 
8.3%
u 446
 
8.3%
g 446
 
8.3%
E 183
 
3.4%
T 96
 
1.8%
o 96
 
1.8%
Other values (5) 480
 
8.9%
Common
ValueCountFrequency (%)
192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1617
29.0%
e 567
 
10.2%
i 542
 
9.7%
d 462
 
8.3%
R 446
 
8.0%
u 446
 
8.0%
g 446
 
8.0%
192
 
3.4%
E 183
 
3.3%
T 96
 
1.7%
Other values (6) 576
 
10.3%

runtime
Real number (ℝ)

High correlation  Missing 

Distinct44
Distinct (%)30.8%
Missing582
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean46.888112
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:20.814138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q120
median30
Q360
95-th percentile147
Maximum300
Range299
Interquartile range (IQR)40

Descriptive statistics

Standard deviation47.046442
Coefficient of variation (CV)1.0033768
Kurtosis8.4507207
Mean46.888112
Median Absolute Deviation (MAD)18
Skewness2.5903004
Sum6705
Variance2213.3677
MonotonicityNot monotonic
2025-03-30T09:27:20.915435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
60 24
 
3.3%
30 19
 
2.6%
20 7
 
1.0%
120 6
 
0.8%
25 6
 
0.8%
45 6
 
0.8%
24 6
 
0.8%
5 5
 
0.7%
12 5
 
0.7%
10 5
 
0.7%
Other values (34) 54
 
7.4%
(Missing) 582
80.3%
ValueCountFrequency (%)
1 2
 
0.3%
2 2
 
0.3%
3 1
 
0.1%
5 5
0.7%
6 1
 
0.1%
8 2
 
0.3%
10 5
0.7%
11 2
 
0.3%
12 5
0.7%
15 2
 
0.3%
ValueCountFrequency (%)
300 1
 
0.1%
240 1
 
0.1%
210 1
 
0.1%
180 3
0.4%
159 1
 
0.1%
150 1
 
0.1%
120 6
0.8%
90 3
0.4%
70 1
 
0.1%
65 1
 
0.1%

averageRuntime
Real number (ℝ)

High correlation  Missing 

Distinct103
Distinct (%)15.5%
Missing62
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean42.297134
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:21.035794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q121.5
median39
Q353
95-th percentile100.9
Maximum300
Range299
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation35.08295
Coefficient of variation (CV)0.82944036
Kurtosis13.164604
Mean42.297134
Median Absolute Deviation (MAD)16
Skewness2.9845712
Sum28043
Variance1230.8134
MonotonicityNot monotonic
2025-03-30T09:27:21.150287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 41
 
5.7%
60 41
 
5.7%
45 40
 
5.5%
25 24
 
3.3%
15 23
 
3.2%
43 23
 
3.2%
10 22
 
3.0%
20 16
 
2.2%
24 14
 
1.9%
12 13
 
1.8%
Other values (93) 406
56.0%
(Missing) 62
 
8.6%
ValueCountFrequency (%)
1 3
 
0.4%
2 6
 
0.8%
3 5
 
0.7%
4 3
 
0.4%
5 7
 
1.0%
6 8
 
1.1%
7 4
 
0.6%
8 7
 
1.0%
9 4
 
0.6%
10 22
3.0%
ValueCountFrequency (%)
300 1
 
0.1%
242 1
 
0.1%
240 3
0.4%
219 1
 
0.1%
194 1
 
0.1%
184 1
 
0.1%
180 5
0.7%
177 1
 
0.1%
164 1
 
0.1%
163 1
 
0.1%

premiered
Date

Distinct476
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum1944-01-20 00:00:00
Maximum2024-11-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T09:27:21.265093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:21.400728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ended
Date

Missing 

Distinct82
Distinct (%)44.8%
Missing542
Missing (%)74.8%
Memory size5.8 KiB
Minimum2024-01-01 00:00:00
Maximum2025-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T09:27:21.522279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:21.648623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

officialSite
Text

Missing 

Distinct640
Distinct (%)99.5%
Missing82
Missing (%)11.3%
Memory size5.8 KiB
2025-03-30T09:27:21.821900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length250
Median length98
Mean length52.015552
Min length16

Characters and Unicode

Total characters33446
Distinct characters97
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique639 ?
Unique (%)99.4%

Sample

1st rowhttps://www.ivi.ru/watch/nezhnost
2nd rowhttps://okko.tv/serial/predposlednjaja-instancija
3rd rowhttps://okko.tv/serial/manjunja
4th rowhttps://wink.ru/series/ne-detskoe-kino-year-2023?ysclid=lpbaiai0cw654763598
5th rowhttps://kion.ru/video/serial/822094895/season/822095042/episode/822094715
ValueCountFrequency (%)
https://abcnews.go.com/live 4
 
0.6%
https://www.itv.com/watch/tiktok-murder-gone-viral/10a2945 1
 
0.2%
https://www.bbc.co.uk/cbeebies/shows/jojo-and-gran 1
 
0.2%
https://okko.tv/serial/manjunja 1
 
0.2%
https://wink.ru/series/ne-detskoe-kino-year-2023?ysclid=lpbaiai0cw654763598 1
 
0.2%
https://kion.ru/video/serial/822094895/season/822095042/episode/822094715 1
 
0.2%
https://premier.one/show/imperatritsy-mini-serial 1
 
0.2%
https://iview.abc.net.au/show/planet-lulin 1
 
0.2%
https://v.qq.com/x/search/?q=无上神帝&stag=&smartbox_ab 1
 
0.2%
https://v.qq.com/x/cover/mzc00200azkttu2.html 1
 
0.2%
Other values (630) 630
98.0%
2025-03-30T09:27:22.114911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2738
 
8.2%
t 2447
 
7.3%
s 1683
 
5.0%
e 1584
 
4.7%
o 1533
 
4.6%
w 1447
 
4.3%
. 1378
 
4.1%
h 1199
 
3.6%
a 1191
 
3.6%
p 1172
 
3.5%
Other values (87) 17074
51.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22394
67.0%
Other Punctuation 5070
 
15.2%
Decimal Number 3461
 
10.3%
Uppercase Letter 1474
 
4.4%
Dash Punctuation 758
 
2.3%
Connector Punctuation 152
 
0.5%
Math Symbol 129
 
0.4%
Other Letter 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2447
 
10.9%
s 1683
 
7.5%
e 1584
 
7.1%
o 1533
 
6.8%
w 1447
 
6.5%
h 1199
 
5.4%
a 1191
 
5.3%
p 1172
 
5.2%
m 1079
 
4.8%
c 1062
 
4.7%
Other values (27) 7997
35.7%
Uppercase Letter
ValueCountFrequency (%)
B 111
 
7.5%
E 106
 
7.2%
P 91
 
6.2%
L 88
 
6.0%
A 80
 
5.4%
T 66
 
4.5%
C 65
 
4.4%
N 62
 
4.2%
O 60
 
4.1%
D 55
 
3.7%
Other values (18) 690
46.8%
Other Punctuation
ValueCountFrequency (%)
/ 2738
54.0%
. 1378
27.2%
: 646
 
12.7%
% 182
 
3.6%
? 74
 
1.5%
& 32
 
0.6%
@ 15
 
0.3%
# 2
 
< 0.1%
, 2
 
< 0.1%
! 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 622
18.0%
2 412
11.9%
1 382
11.0%
8 373
10.8%
4 330
9.5%
3 305
8.8%
5 275
7.9%
6 270
7.8%
9 259
7.5%
7 233
 
6.7%
Other Letter
ValueCountFrequency (%)
上 1
12.5%
无 1
12.5%
帝 1
12.5%
神 1
12.5%
冰 1
12.5%
火 1
12.5%
厨 1
12.5%
魔 1
12.5%
Math Symbol
ValueCountFrequency (%)
= 126
97.7%
~ 3
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 758
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23847
71.3%
Common 9570
28.6%
Cyrillic 21
 
0.1%
Han 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2447
 
10.3%
s 1683
 
7.1%
e 1584
 
6.6%
o 1533
 
6.4%
w 1447
 
6.1%
h 1199
 
5.0%
a 1191
 
5.0%
p 1172
 
4.9%
m 1079
 
4.5%
c 1062
 
4.5%
Other values (42) 9450
39.6%
Common
ValueCountFrequency (%)
/ 2738
28.6%
. 1378
14.4%
- 758
 
7.9%
: 646
 
6.8%
0 622
 
6.5%
2 412
 
4.3%
1 382
 
4.0%
8 373
 
3.9%
4 330
 
3.4%
3 305
 
3.2%
Other values (14) 1626
17.0%
Cyrillic
ValueCountFrequency (%)
а 5
23.8%
н 3
14.3%
к 2
 
9.5%
р 2
 
9.5%
К 1
 
4.8%
г 1
 
4.8%
и 1
 
4.8%
д 1
 
4.8%
м 1
 
4.8%
і 1
 
4.8%
Other values (3) 3
14.3%
Han
ValueCountFrequency (%)
上 1
12.5%
无 1
12.5%
帝 1
12.5%
神 1
12.5%
冰 1
12.5%
火 1
12.5%
厨 1
12.5%
魔 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33417
99.9%
Cyrillic 21
 
0.1%
CJK 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2738
 
8.2%
t 2447
 
7.3%
s 1683
 
5.0%
e 1584
 
4.7%
o 1533
 
4.6%
w 1447
 
4.3%
. 1378
 
4.1%
h 1199
 
3.6%
a 1191
 
3.6%
p 1172
 
3.5%
Other values (66) 17045
51.0%
Cyrillic
ValueCountFrequency (%)
а 5
23.8%
н 3
14.3%
к 2
 
9.5%
р 2
 
9.5%
К 1
 
4.8%
г 1
 
4.8%
и 1
 
4.8%
д 1
 
4.8%
м 1
 
4.8%
і 1
 
4.8%
Other values (3) 3
14.3%
CJK
ValueCountFrequency (%)
上 1
12.5%
无 1
12.5%
帝 1
12.5%
神 1
12.5%
冰 1
12.5%
火 1
12.5%
厨 1
12.5%
魔 1
12.5%

weight
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.431724
Minimum0
Maximum100
Zeros13
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:22.214910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median32
Q359
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.954521
Coefficient of variation (CV)0.80024421
Kurtosis-0.94420858
Mean37.431724
Median Absolute Deviation (MAD)24
Skewness0.56692836
Sum27138
Variance897.2733
MonotonicityNot monotonic
2025-03-30T09:27:22.332661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 46
 
6.3%
13 35
 
4.8%
4 31
 
4.3%
9 27
 
3.7%
8 25
 
3.4%
23 16
 
2.2%
11 15
 
2.1%
37 14
 
1.9%
1 14
 
1.9%
0 13
 
1.8%
Other values (90) 489
67.4%
ValueCountFrequency (%)
0 13
 
1.8%
1 14
 
1.9%
2 7
 
1.0%
3 13
 
1.8%
4 31
4.3%
5 4
 
0.6%
6 10
 
1.4%
7 46
6.3%
8 25
3.4%
9 27
3.7%
ValueCountFrequency (%)
100 1
 
0.1%
99 6
0.8%
98 3
0.4%
97 5
0.7%
96 7
1.0%
95 7
1.0%
94 5
0.7%
93 6
0.8%
92 4
0.6%
91 6
0.8%

network
Unsupported

Missing  Rejected  Unsupported 

Missing725
Missing (%)100.0%
Memory size5.8 KiB

dvdCountry
Unsupported

Missing  Rejected  Unsupported 

Missing725
Missing (%)100.0%
Memory size5.8 KiB

summary
Text

Missing 

Distinct628
Distinct (%)100.0%
Missing97
Missing (%)13.4%
Memory size5.8 KiB
2025-03-30T09:27:22.632665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2103
Median length514
Mean length351.58121
Min length39

Characters and Unicode

Total characters220793
Distinct characters307
Distinct categories14 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique628 ?
Unique (%)100.0%

Sample

1st row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
2nd row<p>Lulin wasn't expecting to develop alien powers, or intergalactic invaders to crash her school science comp, but hey, Year 6 is full of surprises!</p>
3rd row<p>Ten thousand years ago, Muyun's fairy King was secretly accounted for by holding a Zhuxian figure, and after a long sleep, he awakened in the famous "Muyun waste" of the southern Yun Empire in the Land of Heaven. When Muyun first woke up, he was deliberately bothered by the student Miaoxianyu. Muyun easily completed the Miaoxianyu trap, and he gave more and more alchemy skills by analogy, so the Alchemy masters outside the door could not ask for appreciation. Endless back home, Mu Yun learns that he is about to marry Nona Qin Qin Mengyao. Qin Mengyao was cold and toxic, but could not live until he was 20 years old. The marriage was only for the sake of pastoralists and family of Qin. However, under Mu Linchen's enticement, Mu Yun approves the family's issue on the condition of alchemy.</p><p><br /> </p>
4th row<p>The former mighty Gods of the heavens, after ten thousand lifetimes of reincarnation tragically destroyed! The cruel curse, the hatred of ten thousand lives, Tan Yun determined, no longer sink! The most important thing is that you have to be able to get to the top of the world, step by step, stepping on the corpses of your enemies! To kill against the sky, across all the worlds, only I am the supreme!</p>
5th row<p>Of all the races in the world only 3 stand at the top. Each race possesses a master of the martial arts, a Golden Martial God. The balance of power appears to be shifting after Wang Fan's brother is killed triggering a chain of events that will begin to turn the world upside down. Disguising himself as the Silver Tiger King, Wang Fan becomes a symbol of power and the hope of the human race.<br />Some days Wang Fan is a boy genius that tries to blend in as a smart but helpless student. Other days he becomes a powerful tiger. Every race has their eyes on the Silver Tiger King as he continues to surpass every precedent and excel far beyond his peers all while under the guise of an unassuming student.</p>
ValueCountFrequency (%)
the 2244
 
6.2%
and 1295
 
3.6%
of 1083
 
3.0%
a 974
 
2.7%
to 911
 
2.5%
in 703
 
1.9%
is 428
 
1.2%
with 342
 
0.9%
on 281
 
0.8%
his 279
 
0.8%
Other values (8554) 27527
76.3%
2025-03-30T09:27:23.080779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35388
16.0%
e 21151
 
9.6%
t 14164
 
6.4%
a 13481
 
6.1%
i 12720
 
5.8%
n 12502
 
5.7%
o 12402
 
5.6%
s 11533
 
5.2%
r 10690
 
4.8%
h 8923
 
4.0%
Other values (297) 67839
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 168349
76.2%
Space Separator 35445
 
16.1%
Uppercase Letter 6167
 
2.8%
Other Punctuation 5818
 
2.6%
Math Symbol 3547
 
1.6%
Decimal Number 545
 
0.2%
Dash Punctuation 496
 
0.2%
Other Letter 313
 
0.1%
Close Punctuation 49
 
< 0.1%
Open Punctuation 49
 
< 0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
的 16
 
5.1%
龙 10
 
3.2%
古 9
 
2.9%
争 8
 
2.6%
锋 8
 
2.6%
人 7
 
2.2%
在 6
 
1.9%
神 6
 
1.9%
了 5
 
1.6%
为 5
 
1.6%
Other values (165) 233
74.4%
Lowercase Letter
ValueCountFrequency (%)
e 21151
12.6%
t 14164
 
8.4%
a 13481
 
8.0%
i 12720
 
7.6%
n 12502
 
7.4%
o 12402
 
7.4%
s 11533
 
6.9%
r 10690
 
6.3%
h 8923
 
5.3%
l 6923
 
4.1%
Other values (48) 43860
26.1%
Uppercase Letter
ValueCountFrequency (%)
T 626
 
10.2%
S 509
 
8.3%
A 472
 
7.7%
C 353
 
5.7%
M 341
 
5.5%
W 320
 
5.2%
H 305
 
4.9%
L 296
 
4.8%
B 288
 
4.7%
D 266
 
4.3%
Other values (20) 2391
38.8%
Other Punctuation
ValueCountFrequency (%)
, 2117
36.4%
. 1736
29.8%
/ 911
15.7%
' 428
 
7.4%
" 281
 
4.8%
? 105
 
1.8%
! 85
 
1.5%
: 64
 
1.1%
; 37
 
0.6%
， 20
 
0.3%
Other values (6) 34
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 180
33.0%
1 101
18.5%
2 83
15.2%
9 47
 
8.6%
5 32
 
5.9%
3 30
 
5.5%
4 27
 
5.0%
8 21
 
3.9%
6 15
 
2.8%
7 9
 
1.7%
Math Symbol
ValueCountFrequency (%)
< 1773
50.0%
> 1773
50.0%
+ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 445
89.7%
– 26
 
5.2%
— 25
 
5.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
75.0%
€ 1
 
12.5%
£ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
35388
99.8%
  57
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 46
93.9%
] 3
 
6.1%
Open Punctuation
ValueCountFrequency (%)
( 46
93.9%
[ 3
 
6.1%
Initial Punctuation
ValueCountFrequency (%)
‘ 5
100.0%
Modifier Letter
ValueCountFrequency (%)
ー 1
100.0%
Other Symbol
ValueCountFrequency (%)
✪ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174501
79.0%
Common 45964
 
20.8%
Han 309
 
0.1%
Cyrillic 8
 
< 0.1%
Greek 7
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
的 16
 
5.2%
龙 10
 
3.2%
古 9
 
2.9%
争 8
 
2.6%
锋 8
 
2.6%
人 7
 
2.3%
在 6
 
1.9%
神 6
 
1.9%
了 5
 
1.6%
为 5
 
1.6%
Other values (161) 229
74.1%
Latin
ValueCountFrequency (%)
e 21151
12.1%
t 14164
 
8.1%
a 13481
 
7.7%
i 12720
 
7.3%
n 12502
 
7.2%
o 12402
 
7.1%
s 11533
 
6.6%
r 10690
 
6.1%
h 8923
 
5.1%
l 6923
 
4.0%
Other values (64) 50012
28.7%
Common
ValueCountFrequency (%)
35388
77.0%
, 2117
 
4.6%
< 1773
 
3.9%
> 1773
 
3.9%
. 1736
 
3.8%
/ 911
 
2.0%
- 445
 
1.0%
' 428
 
0.9%
" 281
 
0.6%
0 180
 
0.4%
Other values (34) 932
 
2.0%
Cyrillic
ValueCountFrequency (%)
и 2
25.0%
н 1
12.5%
А 1
12.5%
о 1
12.5%
п 1
12.5%
т 1
12.5%
д 1
12.5%
Greek
ValueCountFrequency (%)
Κ 1
14.3%
σ 1
14.3%
τ 1
14.3%
λ 1
14.3%
ω 1
14.3%
έ 1
14.3%
ς 1
14.3%
Katakana
ValueCountFrequency (%)
ム 1
25.0%
ダ 1
25.0%
ス 1
25.0%
タ 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220212
99.7%
CJK 309
 
0.1%
None 186
 
0.1%
Punctuation 71
 
< 0.1%
Cyrillic 8
 
< 0.1%
Katakana 5
 
< 0.1%
Currency Symbols 1
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35388
16.1%
e 21151
 
9.6%
t 14164
 
6.4%
a 13481
 
6.1%
i 12720
 
5.8%
n 12502
 
5.7%
o 12402
 
5.6%
s 11533
 
5.2%
r 10690
 
4.9%
h 8923
 
4.1%
Other values (75) 67258
30.5%
None
ValueCountFrequency (%)
  57
30.6%
ä 22
 
11.8%
， 20
 
10.8%
å 16
 
8.6%
á 14
 
7.5%
ö 12
 
6.5%
é 6
 
3.2%
č 4
 
2.2%
ñ 4
 
2.2%
í 4
 
2.2%
Other values (23) 27
14.5%
Punctuation
ValueCountFrequency (%)
– 26
36.6%
— 25
35.2%
… 15
21.1%
‘ 5
 
7.0%
CJK
ValueCountFrequency (%)
的 16
 
5.2%
龙 10
 
3.2%
古 9
 
2.9%
争 8
 
2.6%
锋 8
 
2.6%
人 7
 
2.3%
在 6
 
1.9%
神 6
 
1.9%
了 5
 
1.6%
为 5
 
1.6%
Other values (161) 229
74.1%
Cyrillic
ValueCountFrequency (%)
и 2
25.0%
н 1
12.5%
А 1
12.5%
о 1
12.5%
п 1
12.5%
т 1
12.5%
д 1
12.5%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%
Katakana
ValueCountFrequency (%)
ー 1
20.0%
ム 1
20.0%
ダ 1
20.0%
ス 1
20.0%
タ 1
20.0%
Dingbats
ValueCountFrequency (%)
✪ 1
100.0%

updated
Real number (ℝ)

Unique 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7252607 × 109
Minimum1.6991738 × 109
Maximum1.7433325 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:23.216804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.6991738 × 109
5-th percentile1.7049509 × 109
Q11.7095891 × 109
median1.7284191 × 109
Q31.7394887 × 109
95-th percentile1.7430975 × 109
Maximum1.7433325 × 109
Range44158738
Interquartile range (IQR)29899557

Descriptive statistics

Standard deviation14612756
Coefficient of variation (CV)0.0084698828
Kurtosis-1.6109237
Mean1.7252607 × 109
Median Absolute Deviation (MAD)13723336
Skewness-0.16943657
Sum1.250814 × 1012
Variance2.1353263 × 1014
MonotonicityNot monotonic
2025-03-30T09:27:23.382798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1710010766 1
 
0.1%
1704215354 1
 
0.1%
1704783571 1
 
0.1%
1703404987 1
 
0.1%
1704019326 1
 
0.1%
1739711321 1
 
0.1%
1741729285 1
 
0.1%
1710244763 1
 
0.1%
1706616200 1
 
0.1%
1724778089 1
 
0.1%
Other values (715) 715
98.6%
ValueCountFrequency (%)
1699173762 1
0.1%
1699196321 1
0.1%
1700067953 1
0.1%
1701776723 1
0.1%
1703096478 1
0.1%
1703404987 1
0.1%
1703852377 1
0.1%
1703934794 1
0.1%
1704019326 1
0.1%
1704104449 1
0.1%
ValueCountFrequency (%)
1743332500 1
0.1%
1743329179 1
0.1%
1743317005 1
0.1%
1743313326 1
0.1%
1743281666 1
0.1%
1743276741 1
0.1%
1743273252 1
0.1%
1743271660 1
0.1%
1743270944 1
0.1%
1743269538 1
0.1%

schedule.time
Date

Missing 

Distinct46
Distinct (%)20.2%
Missing497
Missing (%)68.6%
Memory size5.8 KiB
Minimum2025-03-30 00:00:00
Maximum2025-03-30 23:35:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T09:27:23.842203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:23.977641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

schedule.days
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.8 KiB

rating.average
Real number (ℝ)

High correlation  Missing 

Distinct43
Distinct (%)33.6%
Missing597
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean6.5140625
Minimum1
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:24.117763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.17
Q16.1
median6.8
Q37.4
95-th percentile8
Maximum8.3
Range7.3
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.391523
Coefficient of variation (CV)0.2136183
Kurtosis4.8481334
Mean6.5140625
Median Absolute Deviation (MAD)0.6
Skewness-1.9542425
Sum833.8
Variance1.9363361
MonotonicityNot monotonic
2025-03-30T09:27:24.250679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.8 8
 
1.1%
7 7
 
1.0%
6.3 7
 
1.0%
7.3 7
 
1.0%
7.2 7
 
1.0%
6.6 6
 
0.8%
8 6
 
0.8%
7.4 6
 
0.8%
7.8 5
 
0.7%
6.7 5
 
0.7%
Other values (33) 64
 
8.8%
(Missing) 597
82.3%
ValueCountFrequency (%)
1 2
0.3%
1.3 1
0.1%
2.1 1
0.1%
2.2 1
0.1%
4.1 2
0.3%
4.3 1
0.1%
4.4 1
0.1%
4.6 1
0.1%
4.7 1
0.1%
4.8 1
0.1%
ValueCountFrequency (%)
8.3 1
 
0.1%
8.2 1
 
0.1%
8.1 1
 
0.1%
8 6
0.8%
7.9 3
0.4%
7.8 5
0.7%
7.7 4
0.6%
7.6 3
0.4%
7.5 4
0.6%
7.4 6
0.8%

webChannel.id
Real number (ℝ)

High correlation  Missing 

Distinct152
Distinct (%)21.6%
Missing22
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean194.99004
Minimum1
Maximum688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:24.390888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q121
median114
Q3327
95-th percentile580
Maximum688
Range687
Interquartile range (IQR)306

Descriptive statistics

Standard deviation188.88813
Coefficient of variation (CV)0.96870653
Kurtosis-0.41359311
Mean194.99004
Median Absolute Deviation (MAD)94
Skewness0.85977927
Sum137078
Variance35678.725
MonotonicityNot monotonic
2025-03-30T09:27:24.609580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 104
 
14.3%
104 55
 
7.6%
26 35
 
4.8%
1 35
 
4.8%
118 28
 
3.9%
3 24
 
3.3%
67 23
 
3.2%
190 18
 
2.5%
347 14
 
1.9%
51 14
 
1.9%
Other values (142) 353
48.7%
(Missing) 22
 
3.0%
ValueCountFrequency (%)
1 35
 
4.8%
2 5
 
0.7%
3 24
 
3.3%
11 3
 
0.4%
12 1
 
0.1%
15 4
 
0.6%
20 3
 
0.4%
21 104
14.3%
26 35
 
4.8%
32 1
 
0.1%
ValueCountFrequency (%)
688 1
0.1%
686 1
0.1%
685 2
0.3%
670 1
0.1%
662 1
0.1%
643 1
0.1%
637 1
0.1%
632 1
0.1%
628 1
0.1%
623 2
0.3%

webChannel.name
Text

Missing 

Distinct151
Distinct (%)21.5%
Missing22
Missing (%)3.0%
Memory size5.8 KiB
2025-03-30T09:27:24.798286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.0881935
Min length3

Characters and Unicode

Total characters5686
Distinct characters85
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)10.2%

Sample

1st rowИви
2nd rowOkko
3rd rowOkko
4th rowWink
5th rowKION
ValueCountFrequency (%)
youtube 104
 
10.1%
tencent 55
 
5.4%
qq 55
 
5.4%
tv 46
 
4.5%
bbc 35
 
3.4%
netflix 35
 
3.4%
iplayer 35
 
3.4%
play 35
 
3.4%
youku 28
 
2.7%
video 25
 
2.4%
Other values (183) 572
55.8%
2025-03-30T09:27:25.068787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 513
 
9.0%
i 328
 
5.8%
322
 
5.7%
u 320
 
5.6%
o 299
 
5.3%
T 278
 
4.9%
a 240
 
4.2%
n 237
 
4.2%
l 206
 
3.6%
t 201
 
3.5%
Other values (75) 2742
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3545
62.3%
Uppercase Letter 1735
30.5%
Space Separator 322
 
5.7%
Math Symbol 45
 
0.8%
Decimal Number 22
 
0.4%
Other Punctuation 17
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 513
14.5%
i 328
 
9.3%
u 320
 
9.0%
o 299
 
8.4%
a 240
 
6.8%
n 237
 
6.7%
l 206
 
5.8%
t 201
 
5.7%
r 167
 
4.7%
b 125
 
3.5%
Other values (33) 909
25.6%
Uppercase Letter
ValueCountFrequency (%)
T 278
16.0%
Y 160
9.2%
P 152
 
8.8%
V 141
 
8.1%
Q 133
 
7.7%
B 126
 
7.3%
N 116
 
6.7%
C 89
 
5.1%
I 83
 
4.8%
S 69
 
4.0%
Other values (22) 388
22.4%
Other Punctuation
ValueCountFrequency (%)
. 8
47.1%
' 4
23.5%
! 2
 
11.8%
: 2
 
11.8%
/ 1
 
5.9%
Math Symbol
ValueCountFrequency (%)
+ 43
95.6%
| 2
 
4.4%
Decimal Number
ValueCountFrequency (%)
2 14
63.6%
4 8
36.4%
Space Separator
ValueCountFrequency (%)
322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5114
89.9%
Common 406
 
7.1%
Cyrillic 166
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 513
 
10.0%
i 328
 
6.4%
u 320
 
6.3%
o 299
 
5.8%
T 278
 
5.4%
a 240
 
4.7%
n 237
 
4.6%
l 206
 
4.0%
t 201
 
3.9%
r 167
 
3.3%
Other values (44) 2325
45.5%
Cyrillic
ValueCountFrequency (%)
и 32
19.3%
о 30
18.1%
д 20
12.0%
е 19
11.4%
В 19
11.4%
м 6
 
3.6%
с 5
 
3.0%
н 5
 
3.0%
к 4
 
2.4%
П 4
 
2.4%
Other values (11) 22
13.3%
Common
ValueCountFrequency (%)
322
79.3%
+ 43
 
10.6%
2 14
 
3.4%
. 8
 
2.0%
4 8
 
2.0%
' 4
 
1.0%
| 2
 
0.5%
! 2
 
0.5%
: 2
 
0.5%
/ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5512
96.9%
Cyrillic 166
 
2.9%
None 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 513
 
9.3%
i 328
 
6.0%
322
 
5.8%
u 320
 
5.8%
o 299
 
5.4%
T 278
 
5.0%
a 240
 
4.4%
n 237
 
4.3%
l 206
 
3.7%
t 201
 
3.6%
Other values (50) 2568
46.6%
Cyrillic
ValueCountFrequency (%)
и 32
19.3%
о 30
18.1%
д 20
12.0%
е 19
11.4%
В 19
11.4%
м 6
 
3.6%
с 5
 
3.0%
н 5
 
3.0%
к 4
 
2.4%
П 4
 
2.4%
Other values (11) 22
13.3%
None
ValueCountFrequency (%)
í 4
50.0%
á 2
25.0%
ǒ 1
 
12.5%
ī 1
 
12.5%

webChannel.country.name
Categorical

High correlation  Missing 

Distinct31
Distinct (%)7.2%
Missing292
Missing (%)40.3%
Memory size5.8 KiB
China
107 
United States
97 
Russian Federation
53 
United Kingdom
45 
Norway
25 
Other values (26)
106 

Length

Max length25
Median length18
Mean length10.196305
Min length5

Characters and Unicode

Total characters4415
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.1%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowRussian Federation
4th rowRussian Federation
5th rowRussian Federation

Common Values

ValueCountFrequency (%)
China 107
 
14.8%
United States 97
 
13.4%
Russian Federation 53
 
7.3%
United Kingdom 45
 
6.2%
Norway 25
 
3.4%
Sweden 22
 
3.0%
Korea, Republic of 10
 
1.4%
Canada 10
 
1.4%
India 9
 
1.2%
Czech Republic 7
 
1.0%
Other values (21) 48
 
6.6%
(Missing) 292
40.3%

Length

2025-03-30T09:27:25.161692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 142
21.5%
china 109
16.5%
states 97
14.7%
russian 53
 
8.0%
federation 53
 
8.0%
kingdom 45
 
6.8%
norway 25
 
3.8%
sweden 22
 
3.3%
republic 17
 
2.6%
of 12
 
1.8%
Other values (26) 86
13.0%

Most occurring characters

ValueCountFrequency (%)
n 473
 
10.7%
i 455
 
10.3%
e 453
 
10.3%
a 429
 
9.7%
t 402
 
9.1%
d 288
 
6.5%
228
 
5.2%
s 210
 
4.8%
o 151
 
3.4%
U 143
 
3.2%
Other values (31) 1183
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3526
79.9%
Uppercase Letter 649
 
14.7%
Space Separator 228
 
5.2%
Other Punctuation 12
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 473
13.4%
i 455
12.9%
e 453
12.8%
a 429
12.2%
t 402
11.4%
d 288
8.2%
s 210
6.0%
o 151
 
4.3%
h 119
 
3.4%
r 115
 
3.3%
Other values (13) 431
12.2%
Uppercase Letter
ValueCountFrequency (%)
U 143
22.0%
C 126
19.4%
S 124
19.1%
R 70
10.8%
F 58
8.9%
K 55
 
8.5%
N 26
 
4.0%
I 11
 
1.7%
T 9
 
1.4%
G 6
 
0.9%
Other values (6) 21
 
3.2%
Space Separator
ValueCountFrequency (%)
228
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4175
94.6%
Common 240
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 473
11.3%
i 455
10.9%
e 453
10.9%
a 429
10.3%
t 402
 
9.6%
d 288
 
6.9%
s 210
 
5.0%
o 151
 
3.6%
U 143
 
3.4%
C 126
 
3.0%
Other values (29) 1045
25.0%
Common
ValueCountFrequency (%)
228
95.0%
, 12
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 473
 
10.7%
i 455
 
10.3%
e 453
 
10.3%
a 429
 
9.7%
t 402
 
9.1%
d 288
 
6.5%
228
 
5.2%
s 210
 
4.8%
o 151
 
3.4%
U 143
 
3.2%
Other values (31) 1183
26.8%

webChannel.country.code
Categorical

High correlation  Missing 

Distinct31
Distinct (%)7.2%
Missing292
Missing (%)40.3%
Memory size5.8 KiB
CN
107 
US
97 
RU
53 
GB
45 
NO
25 
Other values (26)
106 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters866
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.1%

Sample

1st rowRU
2nd rowRU
3rd rowRU
4th rowRU
5th rowRU

Common Values

ValueCountFrequency (%)
CN 107
 
14.8%
US 97
 
13.4%
RU 53
 
7.3%
GB 45
 
6.2%
NO 25
 
3.4%
SE 22
 
3.0%
KR 10
 
1.4%
CA 10
 
1.4%
IN 9
 
1.2%
CZ 7
 
1.0%
Other values (21) 48
 
6.6%
(Missing) 292
40.3%

Length

2025-03-30T09:27:25.262078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn 107
24.7%
us 97
22.4%
ru 53
12.2%
gb 45
10.4%
no 25
 
5.8%
se 22
 
5.1%
kr 10
 
2.3%
ca 10
 
2.3%
in 9
 
2.1%
cz 7
 
1.6%
Other values (21) 48
11.1%

Most occurring characters

ValueCountFrequency (%)
U 156
18.0%
N 142
16.4%
C 125
14.4%
S 123
14.2%
R 72
8.3%
B 50
 
5.8%
G 48
 
5.5%
E 37
 
4.3%
O 25
 
2.9%
A 16
 
1.8%
Other values (11) 72
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 866
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 156
18.0%
N 142
16.4%
C 125
14.4%
S 123
14.2%
R 72
8.3%
B 50
 
5.8%
G 48
 
5.5%
E 37
 
4.3%
O 25
 
2.9%
A 16
 
1.8%
Other values (11) 72
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 156
18.0%
N 142
16.4%
C 125
14.4%
S 123
14.2%
R 72
8.3%
B 50
 
5.8%
G 48
 
5.5%
E 37
 
4.3%
O 25
 
2.9%
A 16
 
1.8%
Other values (11) 72
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 156
18.0%
N 142
16.4%
C 125
14.4%
S 123
14.2%
R 72
8.3%
B 50
 
5.8%
G 48
 
5.5%
E 37
 
4.3%
O 25
 
2.9%
A 16
 
1.8%
Other values (11) 72
8.3%

webChannel.country.timezone
Categorical

High correlation  Missing 

Distinct31
Distinct (%)7.2%
Missing292
Missing (%)40.3%
Memory size5.8 KiB
Asia/Shanghai
107 
America/New_York
97 
Asia/Kamchatka
53 
Europe/London
45 
Europe/Oslo
25 
Other values (26)
106 

Length

Max length17
Median length16
Mean length13.879908
Min length10

Characters and Unicode

Total characters6010
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.1%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Kamchatka
4th rowAsia/Kamchatka
5th rowAsia/Kamchatka

Common Values

ValueCountFrequency (%)
Asia/Shanghai 107
 
14.8%
America/New_York 97
 
13.4%
Asia/Kamchatka 53
 
7.3%
Europe/London 45
 
6.2%
Europe/Oslo 25
 
3.4%
Europe/Stockholm 22
 
3.0%
Asia/Seoul 10
 
1.4%
America/Toronto 10
 
1.4%
Asia/Kolkata 9
 
1.2%
Europe/Prague 7
 
1.0%
Other values (21) 48
 
6.6%
(Missing) 292
40.3%

Length

2025-03-30T09:27:25.358894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asia/shanghai 107
24.7%
america/new_york 97
22.4%
asia/kamchatka 53
12.2%
europe/london 45
10.4%
europe/oslo 25
 
5.8%
europe/stockholm 22
 
5.1%
asia/seoul 10
 
2.3%
america/toronto 10
 
2.3%
asia/kolkata 9
 
2.1%
europe/prague 7
 
1.6%
Other values (21) 48
11.1%

Most occurring characters

ValueCountFrequency (%)
a 726
 
12.1%
o 451
 
7.5%
/ 433
 
7.2%
i 433
 
7.2%
e 381
 
6.3%
r 377
 
6.3%
A 301
 
5.0%
h 292
 
4.9%
s 251
 
4.2%
n 239
 
4.0%
Other values (33) 2126
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4517
75.2%
Uppercase Letter 963
 
16.0%
Other Punctuation 433
 
7.2%
Connector Punctuation 97
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 726
16.1%
o 451
10.0%
i 433
9.6%
e 381
 
8.4%
r 377
 
8.3%
h 292
 
6.5%
s 251
 
5.6%
n 239
 
5.3%
k 188
 
4.2%
m 186
 
4.1%
Other values (13) 993
22.0%
Uppercase Letter
ValueCountFrequency (%)
A 301
31.3%
S 144
15.0%
E 134
13.9%
N 97
 
10.1%
Y 97
 
10.1%
K 63
 
6.5%
L 48
 
5.0%
O 25
 
2.6%
T 14
 
1.5%
B 11
 
1.1%
Other values (8) 29
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 433
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5480
91.2%
Common 530
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 726
 
13.2%
o 451
 
8.2%
i 433
 
7.9%
e 381
 
7.0%
r 377
 
6.9%
A 301
 
5.5%
h 292
 
5.3%
s 251
 
4.6%
n 239
 
4.4%
k 188
 
3.4%
Other values (31) 1841
33.6%
Common
ValueCountFrequency (%)
/ 433
81.7%
_ 97
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 726
 
12.1%
o 451
 
7.5%
/ 433
 
7.2%
i 433
 
7.2%
e 381
 
6.3%
r 377
 
6.3%
A 301
 
5.0%
h 292
 
4.9%
s 251
 
4.2%
n 239
 
4.0%
Other values (33) 2126
35.4%

webChannel.officialSite
Text

Missing 

Distinct97
Distinct (%)17.8%
Missing180
Missing (%)24.8%
Memory size5.8 KiB
2025-03-30T09:27:25.529372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length250
Median length41
Mean length24.047706
Min length15

Characters and Unicode

Total characters13106
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)7.9%

Sample

1st rowhttps://www.ivi.ru/
2nd rowhttps://okko.tv/
3rd rowhttps://okko.tv/
4th rowhttps://kion.ru/
5th rowhttps://okko.tv/
ValueCountFrequency (%)
https://www.youtube.com 104
19.1%
https://v.qq.com 55
 
10.1%
https://www.bbc.co.uk/iplayer 35
 
6.4%
https://www.netflix.com 35
 
6.4%
https://www.primevideo.com 24
 
4.4%
https://www.iq.com 23
 
4.2%
https://www.svtplay.se 18
 
3.3%
https://www.peacocktv.com 14
 
2.6%
https://tv.nrk.no 13
 
2.4%
https://www.disneyplus.com 12
 
2.2%
Other values (87) 212
38.9%
2025-03-30T09:27:25.811695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1501
 
11.5%
t 1449
 
11.1%
w 1195
 
9.1%
. 1069
 
8.2%
o 760
 
5.8%
p 737
 
5.6%
s 702
 
5.4%
h 616
 
4.7%
c 579
 
4.4%
: 545
 
4.2%
Other values (38) 3953
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9820
74.9%
Other Punctuation 3143
 
24.0%
Decimal Number 78
 
0.6%
Connector Punctuation 22
 
0.2%
Math Symbol 19
 
0.1%
Dash Punctuation 19
 
0.1%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1449
14.8%
w 1195
12.2%
o 760
 
7.7%
p 737
 
7.5%
s 702
 
7.1%
h 616
 
6.3%
c 579
 
5.9%
e 491
 
5.0%
m 489
 
5.0%
u 353
 
3.6%
Other values (16) 2449
24.9%
Decimal Number
ValueCountFrequency (%)
2 11
14.1%
6 11
14.1%
3 10
12.8%
4 9
11.5%
7 8
10.3%
0 7
9.0%
9 7
9.0%
8 7
9.0%
5 4
 
5.1%
1 4
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/ 1501
47.8%
. 1069
34.0%
: 545
 
17.3%
& 11
 
0.3%
? 8
 
0.3%
@ 7
 
0.2%
% 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L 4
80.0%
I 1
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%
Math Symbol
ValueCountFrequency (%)
= 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9825
75.0%
Common 3281
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1449
14.7%
w 1195
12.2%
o 760
 
7.7%
p 737
 
7.5%
s 702
 
7.1%
h 616
 
6.3%
c 579
 
5.9%
e 491
 
5.0%
m 489
 
5.0%
u 353
 
3.6%
Other values (18) 2454
25.0%
Common
ValueCountFrequency (%)
/ 1501
45.7%
. 1069
32.6%
: 545
 
16.6%
_ 22
 
0.7%
= 19
 
0.6%
- 19
 
0.6%
2 11
 
0.3%
& 11
 
0.3%
6 11
 
0.3%
3 10
 
0.3%
Other values (10) 63
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1501
 
11.5%
t 1449
 
11.1%
w 1195
 
9.1%
. 1069
 
8.2%
o 760
 
5.8%
p 737
 
5.6%
s 702
 
5.4%
h 616
 
4.7%
c 579
 
4.4%
: 545
 
4.2%
Other values (38) 3953
30.2%

externals.tvrage
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)100.0%
Missing702
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean17498.696
Minimum712
Maximum47170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:25.892471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum712
5-th percentile1999.7
Q15036
median15090
Q331222
95-th percentile35682.6
Maximum47170
Range46458
Interquartile range (IQR)26186

Descriptive statistics

Standard deviation14066.247
Coefficient of variation (CV)0.80384543
Kurtosis-1.1312229
Mean17498.696
Median Absolute Deviation (MAD)11672
Skewness0.45531338
Sum402470
Variance1.9785929 × 108
MonotonicityNot monotonic
2025-03-30T09:27:25.977414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3418 1
 
0.1%
712 1
 
0.1%
34149 1
 
0.1%
3256 1
 
0.1%
19056 1
 
0.1%
30951 1
 
0.1%
6659 1
 
0.1%
8531 1
 
0.1%
25100 1
 
0.1%
5152 1
 
0.1%
Other values (13) 13
 
1.8%
(Missing) 702
96.8%
ValueCountFrequency (%)
712 1
0.1%
1888 1
0.1%
3005 1
0.1%
3256 1
0.1%
3418 1
0.1%
4920 1
0.1%
5152 1
0.1%
5199 1
0.1%
6659 1
0.1%
8531 1
0.1%
ValueCountFrequency (%)
47170 1
0.1%
35853 1
0.1%
34149 1
0.1%
33858 1
0.1%
32413 1
0.1%
31493 1
0.1%
30951 1
0.1%
26056 1
0.1%
25100 1
0.1%
19056 1
0.1%

externals.thetvdb
Real number (ℝ)

High correlation  Missing 

Distinct521
Distinct (%)100.0%
Missing204
Missing (%)28.1%
Infinite0
Infinite (%)0.0%
Mean390447.08
Minimum70366
Maximum459449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:26.088434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70366
5-th percentile245981
Q1373577
median422819
Q3442306
95-th percentile445215
Maximum459449
Range389083
Interquartile range (IQR)68729

Descriptive statistics

Standard deviation81041.356
Coefficient of variation (CV)0.20756041
Kurtosis5.459844
Mean390447.08
Median Absolute Deviation (MAD)21176
Skewness-2.3194011
Sum2.0342293 × 108
Variance6.5677014 × 109
MonotonicityNot monotonic
2025-03-30T09:27:26.205967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441935 1
 
0.1%
439696 1
 
0.1%
413379 1
 
0.1%
444536 1
 
0.1%
388383 1
 
0.1%
406729 1
 
0.1%
409158 1
 
0.1%
433335 1
 
0.1%
442384 1
 
0.1%
287953 1
 
0.1%
Other values (511) 511
70.5%
(Missing) 204
 
28.1%
ValueCountFrequency (%)
70366 1
0.1%
71178 1
0.1%
71753 1
0.1%
71756 1
0.1%
72716 1
0.1%
76355 1
0.1%
76719 1
0.1%
76779 1
0.1%
78006 1
0.1%
78419 1
0.1%
ValueCountFrequency (%)
459449 1
0.1%
457794 1
0.1%
457021 1
0.1%
454395 1
0.1%
451493 1
0.1%
449126 1
0.1%
448382 1
0.1%
447745 1
0.1%
447439 1
0.1%
447332 1
0.1%

externals.imdb
Text

Missing 

Distinct372
Distinct (%)100.0%
Missing353
Missing (%)48.7%
Memory size5.8 KiB
2025-03-30T09:27:26.461071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.75
Min length9

Characters and Unicode

Total characters3627
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique372 ?
Unique (%)100.0%

Sample

1st rowtt20603062
2nd rowtt15816496
3rd rowtt19756810
4th rowtt27432264
5th rowtt27801903
ValueCountFrequency (%)
tt27052533 1
 
0.3%
tt30439032 1
 
0.3%
tt6094268 1
 
0.3%
tt27801903 1
 
0.3%
tt19756810 1
 
0.3%
tt27494999 1
 
0.3%
tt27997713 1
 
0.3%
tt28080721 1
 
0.3%
tt25377596 1
 
0.3%
tt14094206 1
 
0.3%
Other values (362) 362
97.3%
2025-03-30T09:27:26.796664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 744
20.5%
2 413
11.4%
0 328
9.0%
1 325
9.0%
4 293
 
8.1%
8 287
 
7.9%
3 281
 
7.7%
6 279
 
7.7%
5 233
 
6.4%
9 226
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2883
79.5%
Lowercase Letter 744
 
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 413
14.3%
0 328
11.4%
1 325
11.3%
4 293
10.2%
8 287
10.0%
3 281
9.7%
6 279
9.7%
5 233
8.1%
9 226
7.8%
7 218
7.6%
Lowercase Letter
ValueCountFrequency (%)
t 744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2883
79.5%
Latin 744
 
20.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 413
14.3%
0 328
11.4%
1 325
11.3%
4 293
10.2%
8 287
10.0%
3 281
9.7%
6 279
9.7%
5 233
8.1%
9 226
7.8%
7 218
7.6%
Latin
ValueCountFrequency (%)
t 744
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 744
20.5%
2 413
11.4%
0 328
9.0%
1 325
9.0%
4 293
 
8.1%
8 287
 
7.9%
3 281
 
7.7%
6 279
 
7.7%
5 233
 
6.4%
9 226
 
6.2%

image.medium
Text

Missing 

Distinct691
Distinct (%)100.0%
Missing34
Missing (%)4.7%
Memory size5.8 KiB
2025-03-30T09:27:27.007970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length72
Median length72
Mean length71.798842
Min length69

Characters and Unicode

Total characters49613
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique691 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/282/707449.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/388/970660.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/383/957782.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/488/1221089.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/496/1241006.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/388/970660.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/383/957782.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/488/1221089.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/507/1269695.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/558/1395332.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/502/1255253.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/499/1248396.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/275/687521.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/76/191077.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_portrait/468/1172395.jpg 1
 
0.1%
Other values (681) 681
98.6%
2025-03-30T09:27:27.308658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 4837
 
9.7%
/ 4837
 
9.7%
a 3455
 
7.0%
m 3455
 
7.0%
i 2764
 
5.6%
s 2764
 
5.6%
p 2764
 
5.6%
. 2073
 
4.2%
o 2073
 
4.2%
e 2073
 
4.2%
Other values (22) 18518
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34550
69.6%
Other Punctuation 7601
 
15.3%
Decimal Number 6771
 
13.6%
Connector Punctuation 691
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4837
14.0%
a 3455
10.0%
m 3455
10.0%
i 2764
 
8.0%
s 2764
 
8.0%
p 2764
 
8.0%
o 2073
 
6.0%
e 2073
 
6.0%
u 1382
 
4.0%
c 1382
 
4.0%
Other values (8) 7601
22.0%
Decimal Number
ValueCountFrequency (%)
1 1161
17.1%
4 862
12.7%
2 831
12.3%
5 818
12.1%
0 631
9.3%
3 571
8.4%
9 552
8.2%
8 474
7.0%
7 454
 
6.7%
6 417
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 4837
63.6%
. 2073
27.3%
: 691
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34550
69.6%
Common 15063
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4837
14.0%
a 3455
10.0%
m 3455
10.0%
i 2764
 
8.0%
s 2764
 
8.0%
p 2764
 
8.0%
o 2073
 
6.0%
e 2073
 
6.0%
u 1382
 
4.0%
c 1382
 
4.0%
Other values (8) 7601
22.0%
Common
ValueCountFrequency (%)
/ 4837
32.1%
. 2073
13.8%
1 1161
 
7.7%
4 862
 
5.7%
2 831
 
5.5%
5 818
 
5.4%
: 691
 
4.6%
_ 691
 
4.6%
0 631
 
4.2%
3 571
 
3.8%
Other values (4) 1897
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4837
 
9.7%
/ 4837
 
9.7%
a 3455
 
7.0%
m 3455
 
7.0%
i 2764
 
5.6%
s 2764
 
5.6%
p 2764
 
5.6%
. 2073
 
4.2%
o 2073
 
4.2%
e 2073
 
4.2%
Other values (22) 18518
37.3%

image.original
Text

Missing 

Distinct691
Distinct (%)100.0%
Missing34
Missing (%)4.7%
Memory size5.8 KiB
2025-03-30T09:27:27.513625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length75
Median length75
Mean length74.798842
Min length72

Characters and Unicode

Total characters51686
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique691 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/282/707449.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/388/970660.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/383/957782.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/488/1221089.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/496/1241006.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/388/970660.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/383/957782.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/488/1221089.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/507/1269695.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/558/1395332.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/502/1255253.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/499/1248396.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/275/687521.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/76/191077.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/468/1172395.jpg 1
 
0.1%
Other values (681) 681
98.6%
2025-03-30T09:27:27.859189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4837
 
9.4%
t 4146
 
8.0%
a 3455
 
6.7%
i 2764
 
5.3%
s 2764
 
5.3%
o 2764
 
5.3%
u 2073
 
4.0%
c 2073
 
4.0%
p 2073
 
4.0%
g 2073
 
4.0%
Other values (23) 22664
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36623
70.9%
Other Punctuation 7601
 
14.7%
Decimal Number 6771
 
13.1%
Connector Punctuation 691
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4146
 
11.3%
a 3455
 
9.4%
i 2764
 
7.5%
s 2764
 
7.5%
o 2764
 
7.5%
u 2073
 
5.7%
c 2073
 
5.7%
p 2073
 
5.7%
g 2073
 
5.7%
e 2073
 
5.7%
Other values (9) 10365
28.3%
Decimal Number
ValueCountFrequency (%)
1 1161
17.1%
4 862
12.7%
2 831
12.3%
5 818
12.1%
0 631
9.3%
3 571
8.4%
9 552
8.2%
8 474
7.0%
7 454
 
6.7%
6 417
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 4837
63.6%
. 2073
27.3%
: 691
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36623
70.9%
Common 15063
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4146
 
11.3%
a 3455
 
9.4%
i 2764
 
7.5%
s 2764
 
7.5%
o 2764
 
7.5%
u 2073
 
5.7%
c 2073
 
5.7%
p 2073
 
5.7%
g 2073
 
5.7%
e 2073
 
5.7%
Other values (9) 10365
28.3%
Common
ValueCountFrequency (%)
/ 4837
32.1%
. 2073
13.8%
1 1161
 
7.7%
4 862
 
5.7%
2 831
 
5.5%
5 818
 
5.4%
: 691
 
4.6%
_ 691
 
4.6%
0 631
 
4.2%
3 571
 
3.8%
Other values (4) 1897
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4837
 
9.4%
t 4146
 
8.0%
a 3455
 
6.7%
i 2764
 
5.3%
s 2764
 
5.3%
o 2764
 
5.3%
u 2073
 
4.0%
c 2073
 
4.0%
p 2073
 
4.0%
g 2073
 
4.0%
Other values (23) 22664
43.8%

_links.self.href
Text

Unique 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:28.041804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.944828
Min length32

Characters and Unicode

Total characters24610
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique725 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/shows/51908
2nd rowhttps://api.tvmaze.com/shows/59205
3rd rowhttps://api.tvmaze.com/shows/59484
4th rowhttps://api.tvmaze.com/shows/72871
5th rowhttps://api.tvmaze.com/shows/73221
ValueCountFrequency (%)
https://api.tvmaze.com/shows/73590 1
 
0.1%
https://api.tvmaze.com/shows/75220 1
 
0.1%
https://api.tvmaze.com/shows/51908 1
 
0.1%
https://api.tvmaze.com/shows/59205 1
 
0.1%
https://api.tvmaze.com/shows/59484 1
 
0.1%
https://api.tvmaze.com/shows/71522 1
 
0.1%
https://api.tvmaze.com/shows/74407 1
 
0.1%
https://api.tvmaze.com/shows/47865 1
 
0.1%
https://api.tvmaze.com/shows/59613 1
 
0.1%
https://api.tvmaze.com/shows/74261 1
 
0.1%
Other values (715) 715
98.6%
2025-03-30T09:27:28.327772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2900
 
11.8%
t 2175
 
8.8%
s 2175
 
8.8%
h 1450
 
5.9%
p 1450
 
5.9%
a 1450
 
5.9%
. 1450
 
5.9%
m 1450
 
5.9%
o 1450
 
5.9%
: 725
 
2.9%
Other values (16) 7935
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15950
64.8%
Other Punctuation 5075
 
20.6%
Decimal Number 3585
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2175
13.6%
s 2175
13.6%
h 1450
9.1%
p 1450
9.1%
a 1450
9.1%
m 1450
9.1%
o 1450
9.1%
v 725
 
4.5%
i 725
 
4.5%
e 725
 
4.5%
Other values (3) 2175
13.6%
Decimal Number
ValueCountFrequency (%)
7 591
16.5%
6 447
12.5%
4 400
11.2%
3 376
10.5%
5 329
9.2%
8 310
8.6%
2 305
8.5%
1 291
8.1%
9 272
7.6%
0 264
7.4%
Other Punctuation
ValueCountFrequency (%)
/ 2900
57.1%
. 1450
28.6%
: 725
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 15950
64.8%
Common 8660
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 2900
33.5%
. 1450
16.7%
: 725
 
8.4%
7 591
 
6.8%
6 447
 
5.2%
4 400
 
4.6%
3 376
 
4.3%
5 329
 
3.8%
8 310
 
3.6%
2 305
 
3.5%
Other values (3) 827
 
9.5%
Latin
ValueCountFrequency (%)
t 2175
13.6%
s 2175
13.6%
h 1450
9.1%
p 1450
9.1%
a 1450
9.1%
m 1450
9.1%
o 1450
9.1%
v 725
 
4.5%
i 725
 
4.5%
e 725
 
4.5%
Other values (3) 2175
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2900
 
11.8%
t 2175
 
8.8%
s 2175
 
8.8%
h 1450
 
5.9%
p 1450
 
5.9%
a 1450
 
5.9%
. 1450
 
5.9%
m 1450
 
5.9%
o 1450
 
5.9%
: 725
 
2.9%
Other values (16) 7935
32.2%

_links.previousepisode.href
Text

Unique 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:28.522284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters28275
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique725 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2730595
2nd rowhttps://api.tvmaze.com/episodes/2719129
3rd rowhttps://api.tvmaze.com/episodes/2718223
4th rowhttps://api.tvmaze.com/episodes/2692657
5th rowhttps://api.tvmaze.com/episodes/3098220
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2730985 1
 
0.1%
https://api.tvmaze.com/episodes/2792942 1
 
0.1%
https://api.tvmaze.com/episodes/2730595 1
 
0.1%
https://api.tvmaze.com/episodes/2719129 1
 
0.1%
https://api.tvmaze.com/episodes/2718223 1
 
0.1%
https://api.tvmaze.com/episodes/2753704 1
 
0.1%
https://api.tvmaze.com/episodes/2760335 1
 
0.1%
https://api.tvmaze.com/episodes/3081380 1
 
0.1%
https://api.tvmaze.com/episodes/2760324 1
 
0.1%
https://api.tvmaze.com/episodes/2759230 1
 
0.1%
Other values (715) 715
98.6%
2025-03-30T09:27:28.810035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2900
 
10.3%
t 2175
 
7.7%
s 2175
 
7.7%
p 2175
 
7.7%
e 2175
 
7.7%
. 1450
 
5.1%
i 1450
 
5.1%
o 1450
 
5.1%
m 1450
 
5.1%
a 1450
 
5.1%
Other values (16) 9425
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18125
64.1%
Other Punctuation 5075
 
17.9%
Decimal Number 5075
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2175
12.0%
s 2175
12.0%
p 2175
12.0%
e 2175
12.0%
i 1450
8.0%
o 1450
8.0%
m 1450
8.0%
a 1450
8.0%
v 725
 
4.0%
h 725
 
4.0%
Other values (3) 2175
12.0%
Decimal Number
ValueCountFrequency (%)
2 777
15.3%
3 702
13.8%
7 618
12.2%
1 527
10.4%
8 441
8.7%
0 436
8.6%
4 428
8.4%
6 390
7.7%
9 379
7.5%
5 377
7.4%
Other Punctuation
ValueCountFrequency (%)
/ 2900
57.1%
. 1450
28.6%
: 725
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 18125
64.1%
Common 10150
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 2900
28.6%
. 1450
14.3%
2 777
 
7.7%
: 725
 
7.1%
3 702
 
6.9%
7 618
 
6.1%
1 527
 
5.2%
8 441
 
4.3%
0 436
 
4.3%
4 428
 
4.2%
Other values (3) 1146
 
11.3%
Latin
ValueCountFrequency (%)
t 2175
12.0%
s 2175
12.0%
p 2175
12.0%
e 2175
12.0%
i 1450
8.0%
o 1450
8.0%
m 1450
8.0%
a 1450
8.0%
v 725
 
4.0%
h 725
 
4.0%
Other values (3) 2175
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2900
 
10.3%
t 2175
 
7.7%
s 2175
 
7.7%
p 2175
 
7.7%
e 2175
 
7.7%
. 1450
 
5.1%
i 1450
 
5.1%
o 1450
 
5.1%
m 1450
 
5.1%
a 1450
 
5.1%
Other values (16) 9425
33.3%

_links.previousepisode.name
Text

Distinct554
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:29.113493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length120
Median length92
Mean length19.486897
Min length2

Characters and Unicode

Total characters14128
Distinct characters237
Distinct categories13 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique503 ?
Unique (%)69.4%

Sample

1st rowСерия 10
2nd rowСерия 30
3rd rowСерия 10
4th rowСерия 12
5th rowСерия 10
ValueCountFrequency (%)
episode 233
 
9.1%
the 96
 
3.8%
50
 
2.0%
серия 31
 
1.2%
24 27
 
1.1%
6 27
 
1.1%
8 26
 
1.0%
to 21
 
0.8%
and 20
 
0.8%
of 19
 
0.7%
Other values (1431) 2006
78.5%
2025-03-30T09:27:29.569850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1831
 
13.0%
e 1016
 
7.2%
i 709
 
5.0%
o 696
 
4.9%
s 604
 
4.3%
a 588
 
4.2%
n 523
 
3.7%
t 442
 
3.1%
d 440
 
3.1%
r 421
 
3.0%
Other values (227) 6858
48.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8967
63.5%
Uppercase Letter 2043
 
14.5%
Space Separator 1831
 
13.0%
Decimal Number 812
 
5.7%
Other Punctuation 274
 
1.9%
Other Letter 96
 
0.7%
Dash Punctuation 37
 
0.3%
Math Symbol 26
 
0.2%
Open Punctuation 15
 
0.1%
Close Punctuation 15
 
0.1%
Other values (3) 12
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1016
 
11.3%
i 709
 
7.9%
o 696
 
7.8%
s 604
 
6.7%
a 588
 
6.6%
n 523
 
5.8%
t 442
 
4.9%
d 440
 
4.9%
r 421
 
4.7%
p 345
 
3.8%
Other values (89) 3183
35.5%
Uppercase Letter
ValueCountFrequency (%)
E 317
 
15.5%
T 144
 
7.0%
S 130
 
6.4%
A 115
 
5.6%
C 87
 
4.3%
B 83
 
4.1%
P 79
 
3.9%
M 77
 
3.8%
R 76
 
3.7%
I 67
 
3.3%
Other values (50) 868
42.5%
Other Letter
ValueCountFrequency (%)
ل 9
 
9.4%
ا 9
 
9.4%
و 8
 
8.3%
ة 6
 
6.2%
د 5
 
5.2%
م 4
 
4.2%
ع 4
 
4.2%
ر 4
 
4.2%
ب 4
 
4.2%
أ 4
 
4.2%
Other values (31) 39
40.6%
Other Punctuation
ValueCountFrequency (%)
, 80
29.2%
. 50
18.2%
: 32
 
11.7%
' 32
 
11.7%
# 23
 
8.4%
! 14
 
5.1%
? 14
 
5.1%
/ 10
 
3.6%
" 10
 
3.6%
& 6
 
2.2%
Other values (2) 3
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 161
19.8%
1 149
18.3%
0 91
11.2%
4 90
11.1%
6 81
10.0%
3 73
9.0%
5 53
 
6.5%
8 50
 
6.2%
7 35
 
4.3%
9 29
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 26
70.3%
– 8
 
21.6%
— 3
 
8.1%
Math Symbol
ValueCountFrequency (%)
| 22
84.6%
~ 3
 
11.5%
+ 1
 
3.8%
Open Punctuation
ValueCountFrequency (%)
( 13
86.7%
[ 2
 
13.3%
Close Punctuation
ValueCountFrequency (%)
) 13
86.7%
] 2
 
13.3%
Initial Punctuation
ValueCountFrequency (%)
« 5
83.3%
‘ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
1831
100.0%
Final Punctuation
ValueCountFrequency (%)
» 5
100.0%
Other Symbol
ValueCountFrequency (%)
№ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9296
65.8%
Common 3022
 
21.4%
Cyrillic 1663
 
11.8%
Arabic 79
 
0.6%
Greek 30
 
0.2%
Armenian 21
 
0.1%
Hangul 17
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1016
 
10.9%
i 709
 
7.6%
o 696
 
7.5%
s 604
 
6.5%
a 588
 
6.3%
n 523
 
5.6%
t 442
 
4.8%
d 440
 
4.7%
r 421
 
4.5%
p 345
 
3.7%
Other values (58) 3512
37.8%
Cyrillic
ValueCountFrequency (%)
а 136
 
8.2%
е 128
 
7.7%
р 125
 
7.5%
и 119
 
7.2%
о 109
 
6.6%
т 77
 
4.6%
н 77
 
4.6%
в 64
 
3.8%
я 59
 
3.5%
с 57
 
3.4%
Other values (51) 712
42.8%
Common
ValueCountFrequency (%)
1831
60.6%
2 161
 
5.3%
1 149
 
4.9%
0 91
 
3.0%
4 90
 
3.0%
6 81
 
2.7%
, 80
 
2.6%
3 73
 
2.4%
5 53
 
1.8%
. 50
 
1.7%
Other values (27) 363
 
12.0%
Arabic
ValueCountFrequency (%)
ل 9
11.4%
ا 9
11.4%
و 8
 
10.1%
ة 6
 
7.6%
د 5
 
6.3%
م 4
 
5.1%
ع 4
 
5.1%
ر 4
 
5.1%
ب 4
 
5.1%
أ 4
 
5.1%
Other values (15) 22
27.8%
Greek
ValueCountFrequency (%)
α 4
13.3%
κ 3
 
10.0%
ό 2
 
6.7%
ο 2
 
6.7%
ν 2
 
6.7%
ε 2
 
6.7%
γ 2
 
6.7%
τ 2
 
6.7%
ώ 2
 
6.7%
Δ 1
 
3.3%
Other values (8) 8
26.7%
Hangul
ValueCountFrequency (%)
회 2
 
11.8%
문 1
 
5.9%
때 1
 
5.9%
신 1
 
5.9%
당 1
 
5.9%
장 1
 
5.9%
왕 1
 
5.9%
난 1
 
5.9%
에 1
 
5.9%
어 1
 
5.9%
Other values (6) 6
35.3%
Armenian
ValueCountFrequency (%)
ր 6
28.6%
ե 3
14.3%
կ 2
 
9.5%
ք 2
 
9.5%
ն 1
 
4.8%
Օ 1
 
4.8%
ի 1
 
4.8%
դ 1
 
4.8%
ս 1
 
4.8%
ծ 1
 
4.8%
Other values (2) 2
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12234
86.6%
Cyrillic 1663
 
11.8%
None 98
 
0.7%
Arabic 79
 
0.6%
Armenian 21
 
0.1%
Hangul 17
 
0.1%
Punctuation 15
 
0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1831
 
15.0%
e 1016
 
8.3%
i 709
 
5.8%
o 696
 
5.7%
s 604
 
4.9%
a 588
 
4.8%
n 523
 
4.3%
t 442
 
3.6%
d 440
 
3.6%
r 421
 
3.4%
Other values (71) 4964
40.6%
Cyrillic
ValueCountFrequency (%)
а 136
 
8.2%
е 128
 
7.7%
р 125
 
7.5%
и 119
 
7.2%
о 109
 
6.6%
т 77
 
4.6%
н 77
 
4.6%
в 64
 
3.8%
я 59
 
3.5%
с 57
 
3.4%
Other values (51) 712
42.8%
None
ValueCountFrequency (%)
ä 12
 
12.2%
ö 9
 
9.2%
ø 7
 
7.1%
á 7
 
7.1%
ü 6
 
6.1%
» 5
 
5.1%
« 5
 
5.1%
å 4
 
4.1%
α 4
 
4.1%
κ 3
 
3.1%
Other values (26) 36
36.7%
Arabic
ValueCountFrequency (%)
ل 9
11.4%
ا 9
11.4%
و 8
 
10.1%
ة 6
 
7.6%
د 5
 
6.3%
م 4
 
5.1%
ع 4
 
5.1%
ر 4
 
5.1%
ب 4
 
5.1%
أ 4
 
5.1%
Other values (15) 22
27.8%
Punctuation
ValueCountFrequency (%)
– 8
53.3%
— 3
 
20.0%
… 2
 
13.3%
• 1
 
6.7%
‘ 1
 
6.7%
Armenian
ValueCountFrequency (%)
ր 6
28.6%
ե 3
14.3%
կ 2
 
9.5%
ք 2
 
9.5%
ն 1
 
4.8%
Օ 1
 
4.8%
ի 1
 
4.8%
դ 1
 
4.8%
ս 1
 
4.8%
ծ 1
 
4.8%
Other values (2) 2
 
9.5%
Hangul
ValueCountFrequency (%)
회 2
 
11.8%
문 1
 
5.9%
때 1
 
5.9%
신 1
 
5.9%
당 1
 
5.9%
장 1
 
5.9%
왕 1
 
5.9%
난 1
 
5.9%
에 1
 
5.9%
어 1
 
5.9%
Other values (6) 6
35.3%
Letterlike Symbols
ValueCountFrequency (%)
№ 1
100.0%

image
Unsupported

Missing  Rejected  Unsupported 

Missing725
Missing (%)100.0%
Memory size5.8 KiB

_links.nextepisode.href
Text

Missing 

Distinct64
Distinct (%)100.0%
Missing661
Missing (%)91.2%
Memory size5.8 KiB
2025-03-30T09:27:29.758706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters2496
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/3145311
2nd rowhttps://api.tvmaze.com/episodes/3135345
3rd rowhttps://api.tvmaze.com/episodes/3153023
4th rowhttps://api.tvmaze.com/episodes/3183363
5th rowhttps://api.tvmaze.com/episodes/3183338
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/3145311 1
 
1.6%
https://api.tvmaze.com/episodes/3135345 1
 
1.6%
https://api.tvmaze.com/episodes/3153023 1
 
1.6%
https://api.tvmaze.com/episodes/3183363 1
 
1.6%
https://api.tvmaze.com/episodes/3183338 1
 
1.6%
https://api.tvmaze.com/episodes/3180406 1
 
1.6%
https://api.tvmaze.com/episodes/3159123 1
 
1.6%
https://api.tvmaze.com/episodes/3163723 1
 
1.6%
https://api.tvmaze.com/episodes/3184980 1
 
1.6%
https://api.tvmaze.com/episodes/3167831 1
 
1.6%
Other values (54) 54
84.4%
2025-03-30T09:27:30.120981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 256
 
10.3%
t 192
 
7.7%
s 192
 
7.7%
p 192
 
7.7%
e 192
 
7.7%
. 128
 
5.1%
i 128
 
5.1%
o 128
 
5.1%
m 128
 
5.1%
a 128
 
5.1%
Other values (16) 832
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1600
64.1%
Other Punctuation 448
 
17.9%
Decimal Number 448
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 192
12.0%
s 192
12.0%
p 192
12.0%
e 192
12.0%
i 128
8.0%
o 128
8.0%
m 128
8.0%
a 128
8.0%
h 64
 
4.0%
v 64
 
4.0%
Other values (3) 192
12.0%
Decimal Number
ValueCountFrequency (%)
3 106
23.7%
1 85
19.0%
8 50
11.2%
5 35
 
7.8%
2 33
 
7.4%
4 31
 
6.9%
7 31
 
6.9%
0 29
 
6.5%
6 29
 
6.5%
9 19
 
4.2%
Other Punctuation
ValueCountFrequency (%)
/ 256
57.1%
. 128
28.6%
: 64
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1600
64.1%
Common 896
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 256
28.6%
. 128
14.3%
3 106
11.8%
1 85
 
9.5%
: 64
 
7.1%
8 50
 
5.6%
5 35
 
3.9%
2 33
 
3.7%
4 31
 
3.5%
7 31
 
3.5%
Other values (3) 77
 
8.6%
Latin
ValueCountFrequency (%)
t 192
12.0%
s 192
12.0%
p 192
12.0%
e 192
12.0%
i 128
8.0%
o 128
8.0%
m 128
8.0%
a 128
8.0%
h 64
 
4.0%
v 64
 
4.0%
Other values (3) 192
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 256
 
10.3%
t 192
 
7.7%
s 192
 
7.7%
p 192
 
7.7%
e 192
 
7.7%
. 128
 
5.1%
i 128
 
5.1%
o 128
 
5.1%
m 128
 
5.1%
a 128
 
5.1%
Other values (16) 832
33.3%

_links.nextepisode.name
Text

Missing 

Distinct61
Distinct (%)95.3%
Missing661
Missing (%)91.2%
Memory size5.8 KiB
2025-03-30T09:27:30.406892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length36
Mean length16.078125
Min length3

Characters and Unicode

Total characters1029
Distinct characters91
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)92.2%

Sample

1st rowEpisode 470
2nd rowEpisode 397
3rd rowEpisode 82
4th row31/03/2025
5th rowEpisode 9216
ValueCountFrequency (%)
episode 26
 
14.1%
in 5
 
2.7%
4
 
2.2%
1 3
 
1.6%
tba 3
 
1.6%
13 2
 
1.1%
14 2
 
1.1%
night 2
 
1.1%
the 2
 
1.1%
2025 2
 
1.1%
Other values (132) 134
72.4%
2025-03-30T09:27:30.820318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
 
11.8%
e 77
 
7.5%
i 65
 
6.3%
o 55
 
5.3%
s 50
 
4.9%
d 47
 
4.6%
n 42
 
4.1%
p 39
 
3.8%
a 37
 
3.6%
E 34
 
3.3%
Other values (81) 462
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 601
58.4%
Uppercase Letter 141
 
13.7%
Decimal Number 126
 
12.2%
Space Separator 121
 
11.8%
Other Punctuation 30
 
2.9%
Dash Punctuation 8
 
0.8%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 77
12.8%
i 65
10.8%
o 55
9.2%
s 50
 
8.3%
d 47
 
7.8%
n 42
 
7.0%
p 39
 
6.5%
a 37
 
6.2%
r 27
 
4.5%
t 26
 
4.3%
Other values (32) 136
22.6%
Uppercase Letter
ValueCountFrequency (%)
E 34
24.1%
T 12
 
8.5%
M 9
 
6.4%
A 9
 
6.4%
S 9
 
6.4%
P 8
 
5.7%
B 7
 
5.0%
W 7
 
5.0%
K 5
 
3.5%
N 5
 
3.5%
Other values (17) 36
25.5%
Decimal Number
ValueCountFrequency (%)
1 22
17.5%
2 17
13.5%
3 16
12.7%
5 14
11.1%
0 13
10.3%
6 13
10.3%
4 13
10.3%
7 7
 
5.6%
9 7
 
5.6%
8 4
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 8
26.7%
, 7
23.3%
/ 4
13.3%
# 3
 
10.0%
: 2
 
6.7%
' 2
 
6.7%
? 2
 
6.7%
! 2
 
6.7%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 716
69.6%
Common 287
27.9%
Cyrillic 26
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 77
 
10.8%
i 65
 
9.1%
o 55
 
7.7%
s 50
 
7.0%
d 47
 
6.6%
n 42
 
5.9%
p 39
 
5.4%
a 37
 
5.2%
E 34
 
4.7%
r 27
 
3.8%
Other values (42) 243
33.9%
Common
ValueCountFrequency (%)
121
42.2%
1 22
 
7.7%
2 17
 
5.9%
3 16
 
5.6%
5 14
 
4.9%
0 13
 
4.5%
6 13
 
4.5%
4 13
 
4.5%
. 8
 
2.8%
- 8
 
2.8%
Other values (12) 42
 
14.6%
Cyrillic
ValueCountFrequency (%)
я 4
15.4%
е 4
15.4%
и 2
 
7.7%
д 2
 
7.7%
к 2
 
7.7%
С 1
 
3.8%
р 1
 
3.8%
л 1
 
3.8%
ц 1
 
3.8%
а 1
 
3.8%
Other values (7) 7
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 998
97.0%
Cyrillic 26
 
2.5%
None 5
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
 
12.1%
e 77
 
7.7%
i 65
 
6.5%
o 55
 
5.5%
s 50
 
5.0%
d 47
 
4.7%
n 42
 
4.2%
p 39
 
3.9%
a 37
 
3.7%
E 34
 
3.4%
Other values (60) 431
43.2%
Cyrillic
ValueCountFrequency (%)
я 4
15.4%
е 4
15.4%
и 2
 
7.7%
д 2
 
7.7%
к 2
 
7.7%
С 1
 
3.8%
р 1
 
3.8%
л 1
 
3.8%
ц 1
 
3.8%
а 1
 
3.8%
Other values (7) 7
26.9%
None
ValueCountFrequency (%)
å 2
40.0%
ó 1
20.0%
æ 1
20.0%
ø 1
20.0%

network.id
Real number (ℝ)

High correlation  Missing 

Distinct41
Distinct (%)77.4%
Missing672
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean452.03774
Minimum1
Maximum1963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-03-30T09:27:30.974301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q178
median205
Q3758
95-th percentile1573.8
Maximum1963
Range1962
Interquartile range (IQR)680

Descriptive statistics

Standard deviation533.86922
Coefficient of variation (CV)1.181028
Kurtosis0.66966299
Mean452.03774
Median Absolute Deviation (MAD)176
Skewness1.324308
Sum23958
Variance285016.34
MonotonicityNot monotonic
2025-03-30T09:27:31.082278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1039 4
 
0.6%
308 3
 
0.4%
280 2
 
0.3%
1282 2
 
0.3%
2 2
 
0.3%
12 2
 
0.3%
132 2
 
0.3%
1 2
 
0.3%
481 2
 
0.3%
3 1
 
0.1%
Other values (31) 31
 
4.3%
(Missing) 672
92.7%
ValueCountFrequency (%)
1 2
0.3%
2 2
0.3%
3 1
0.1%
5 1
0.1%
12 2
0.3%
29 1
0.1%
36 1
0.1%
40 1
0.1%
52 1
0.1%
76 1
0.1%
ValueCountFrequency (%)
1963 1
 
0.1%
1766 1
 
0.1%
1683 1
 
0.1%
1501 1
 
0.1%
1328 1
 
0.1%
1282 2
0.3%
1058 1
 
0.1%
1039 4
0.6%
790 1
 
0.1%
758 1
 
0.1%

network.name
Text

Missing 

Distinct40
Distinct (%)75.5%
Missing672
Missing (%)92.7%
Memory size5.8 KiB
2025-03-30T09:27:31.250441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length20
Mean length6.9622642
Min length3

Characters and Unicode

Total characters369
Distinct characters62
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)56.6%

Sample

1st rowMBC
2nd rowBeijing TV
3rd rowtvN
4th rowCCTV-1
5th rowCBC
ValueCountFrequency (%)
tv 6
 
7.6%
disney 5
 
6.3%
junior 4
 
5.1%
тнт 3
 
3.8%
tokyo 3
 
3.8%
beijing 2
 
2.5%
tvn 2
 
2.5%
abc 2
 
2.5%
mx 2
 
2.5%
cctv-1 2
 
2.5%
Other values (42) 48
60.8%
2025-03-30T09:27:31.522935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
7.0%
n 24
 
6.5%
C 24
 
6.5%
e 20
 
5.4%
T 20
 
5.4%
i 19
 
5.1%
o 18
 
4.9%
B 18
 
4.9%
a 16
 
4.3%
N 12
 
3.3%
Other values (52) 172
46.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180
48.8%
Uppercase Letter 146
39.6%
Space Separator 26
 
7.0%
Decimal Number 10
 
2.7%
Dash Punctuation 6
 
1.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 24
13.3%
e 20
11.1%
i 19
10.6%
o 18
10.0%
a 16
 
8.9%
y 9
 
5.0%
r 9
 
5.0%
l 8
 
4.4%
s 7
 
3.9%
t 6
 
3.3%
Other values (20) 44
24.4%
Uppercase Letter
ValueCountFrequency (%)
C 24
16.4%
T 20
13.7%
B 18
12.3%
N 12
8.2%
V 11
 
7.5%
D 8
 
5.5%
Т 7
 
4.8%
S 7
 
4.8%
M 5
 
3.4%
A 5
 
3.4%
Other values (14) 29
19.9%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
2 3
30.0%
3 2
20.0%
4 1
 
10.0%
8 1
 
10.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 305
82.7%
Common 43
 
11.7%
Cyrillic 21
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 24
 
7.9%
C 24
 
7.9%
e 20
 
6.6%
T 20
 
6.6%
i 19
 
6.2%
o 18
 
5.9%
B 18
 
5.9%
a 16
 
5.2%
N 12
 
3.9%
V 11
 
3.6%
Other values (32) 123
40.3%
Cyrillic
ValueCountFrequency (%)
Т 7
33.3%
Н 3
14.3%
а 2
 
9.5%
т 1
 
4.8%
я 1
 
4.8%
П 1
 
4.8%
В 1
 
4.8%
ы 1
 
4.8%
й 1
 
4.8%
к 1
 
4.8%
Other values (2) 2
 
9.5%
Common
ValueCountFrequency (%)
26
60.5%
- 6
 
14.0%
1 3
 
7.0%
2 3
 
7.0%
3 2
 
4.7%
4 1
 
2.3%
8 1
 
2.3%
& 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346
93.8%
Cyrillic 21
 
5.7%
None 2
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
 
7.5%
n 24
 
6.9%
C 24
 
6.9%
e 20
 
5.8%
T 20
 
5.8%
i 19
 
5.5%
o 18
 
5.2%
B 18
 
5.2%
a 16
 
4.6%
N 12
 
3.5%
Other values (39) 149
43.1%
Cyrillic
ValueCountFrequency (%)
Т 7
33.3%
Н 3
14.3%
а 2
 
9.5%
т 1
 
4.8%
я 1
 
4.8%
П 1
 
4.8%
В 1
 
4.8%
ы 1
 
4.8%
й 1
 
4.8%
к 1
 
4.8%
Other values (2) 2
 
9.5%
None
ValueCountFrequency (%)
é 2
100.0%

network.country.name
Categorical

High correlation  Missing 

Distinct14
Distinct (%)26.4%
Missing672
Missing (%)92.7%
Memory size5.8 KiB
United States
19 
China
7 
Russian Federation
5 
Korea, Republic of
4 
Denmark
4 
Other values (9)
14 

Length

Max length18
Median length14
Mean length11.113208
Min length5

Characters and Unicode

Total characters589
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)11.3%

Sample

1st rowKorea, Republic of
2nd rowChina
3rd rowKorea, Republic of
4th rowChina
5th rowCanada

Common Values

ValueCountFrequency (%)
United States 19
 
2.6%
China 7
 
1.0%
Russian Federation 5
 
0.7%
Korea, Republic of 4
 
0.6%
Denmark 4
 
0.6%
Japan 4
 
0.6%
Canada 2
 
0.3%
United Kingdom 2
 
0.3%
Czech Republic 1
 
0.1%
Netherlands 1
 
0.1%
Other values (4) 4
 
0.6%
(Missing) 672
92.7%

Length

2025-03-30T09:27:31.620656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 21
23.6%
states 19
21.3%
china 7
 
7.9%
russian 5
 
5.6%
federation 5
 
5.6%
republic 5
 
5.6%
korea 4
 
4.5%
of 4
 
4.5%
denmark 4
 
4.5%
japan 4
 
4.5%
Other values (9) 11
12.4%

Most occurring characters

ValueCountFrequency (%)
e 67
11.4%
t 67
11.4%
a 65
11.0%
n 52
 
8.8%
i 48
 
8.1%
36
 
6.1%
d 32
 
5.4%
s 31
 
5.3%
U 21
 
3.6%
S 20
 
3.4%
Other values (24) 150
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 464
78.8%
Uppercase Letter 85
 
14.4%
Space Separator 36
 
6.1%
Other Punctuation 4
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 67
14.4%
t 67
14.4%
a 65
14.0%
n 52
11.2%
i 48
10.3%
d 32
6.9%
s 31
6.7%
r 17
 
3.7%
o 15
 
3.2%
u 12
 
2.6%
Other values (11) 58
12.5%
Uppercase Letter
ValueCountFrequency (%)
U 21
24.7%
S 20
23.5%
C 10
11.8%
R 10
11.8%
F 6
 
7.1%
K 6
 
7.1%
D 4
 
4.7%
J 4
 
4.7%
A 2
 
2.4%
N 1
 
1.2%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 549
93.2%
Common 40
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 67
12.2%
t 67
12.2%
a 65
11.8%
n 52
9.5%
i 48
 
8.7%
d 32
 
5.8%
s 31
 
5.6%
U 21
 
3.8%
S 20
 
3.6%
r 17
 
3.1%
Other values (22) 129
23.5%
Common
ValueCountFrequency (%)
36
90.0%
, 4
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 67
11.4%
t 67
11.4%
a 65
11.0%
n 52
 
8.8%
i 48
 
8.1%
36
 
6.1%
d 32
 
5.4%
s 31
 
5.3%
U 21
 
3.6%
S 20
 
3.4%
Other values (24) 150
25.5%

network.country.code
Categorical

High correlation  Missing 

Distinct14
Distinct (%)26.4%
Missing672
Missing (%)92.7%
Memory size5.8 KiB
US
19 
CN
7 
RU
5 
KR
4 
DK
4 
Other values (9)
14 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)11.3%

Sample

1st rowKR
2nd rowCN
3rd rowKR
4th rowCN
5th rowCA

Common Values

ValueCountFrequency (%)
US 19
 
2.6%
CN 7
 
1.0%
RU 5
 
0.7%
KR 4
 
0.6%
DK 4
 
0.6%
JP 4
 
0.6%
CA 2
 
0.3%
GB 2
 
0.3%
CZ 1
 
0.1%
NL 1
 
0.1%
Other values (4) 4
 
0.6%
(Missing) 672
92.7%

Length

2025-03-30T09:27:31.732840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 19
35.8%
cn 7
 
13.2%
ru 5
 
9.4%
kr 4
 
7.5%
dk 4
 
7.5%
jp 4
 
7.5%
ca 2
 
3.8%
gb 2
 
3.8%
cz 1
 
1.9%
nl 1
 
1.9%
Other values (4) 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
U 25
23.6%
S 20
18.9%
C 10
 
9.4%
R 10
 
9.4%
N 8
 
7.5%
K 8
 
7.5%
D 4
 
3.8%
J 4
 
3.8%
P 4
 
3.8%
A 4
 
3.8%
Other values (6) 9
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 106
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 25
23.6%
S 20
18.9%
C 10
 
9.4%
R 10
 
9.4%
N 8
 
7.5%
K 8
 
7.5%
D 4
 
3.8%
J 4
 
3.8%
P 4
 
3.8%
A 4
 
3.8%
Other values (6) 9
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 25
23.6%
S 20
18.9%
C 10
 
9.4%
R 10
 
9.4%
N 8
 
7.5%
K 8
 
7.5%
D 4
 
3.8%
J 4
 
3.8%
P 4
 
3.8%
A 4
 
3.8%
Other values (6) 9
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 25
23.6%
S 20
18.9%
C 10
 
9.4%
R 10
 
9.4%
N 8
 
7.5%
K 8
 
7.5%
D 4
 
3.8%
J 4
 
3.8%
P 4
 
3.8%
A 4
 
3.8%
Other values (6) 9
 
8.5%

network.country.timezone
Categorical

High correlation  Missing 

Distinct14
Distinct (%)26.4%
Missing672
Missing (%)92.7%
Memory size5.8 KiB
America/New_York
19 
Asia/Shanghai
7 
Asia/Kamchatka
5 
Asia/Seoul
4 
Europe/Copenhagen
4 
Other values (9)
14 

Length

Max length17
Median length16
Mean length14.132075
Min length10

Characters and Unicode

Total characters749
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)11.3%

Sample

1st rowAsia/Seoul
2nd rowAsia/Shanghai
3rd rowAsia/Seoul
4th rowAsia/Shanghai
5th rowAmerica/Toronto

Common Values

ValueCountFrequency (%)
America/New_York 19
 
2.6%
Asia/Shanghai 7
 
1.0%
Asia/Kamchatka 5
 
0.7%
Asia/Seoul 4
 
0.6%
Europe/Copenhagen 4
 
0.6%
Asia/Tokyo 4
 
0.6%
America/Toronto 2
 
0.3%
Europe/London 2
 
0.3%
Europe/Prague 1
 
0.1%
Europe/Amsterdam 1
 
0.1%
Other values (4) 4
 
0.6%
(Missing) 672
92.7%

Length

2025-03-30T09:27:31.828677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
america/new_york 19
35.8%
asia/shanghai 7
 
13.2%
asia/kamchatka 5
 
9.4%
asia/seoul 4
 
7.5%
europe/copenhagen 4
 
7.5%
asia/tokyo 4
 
7.5%
america/toronto 2
 
3.8%
europe/london 2
 
3.8%
europe/prague 1
 
1.9%
europe/amsterdam 1
 
1.9%
Other values (4) 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
a 83
 
11.1%
e 64
 
8.5%
r 57
 
7.6%
o 55
 
7.3%
i 54
 
7.2%
/ 53
 
7.1%
A 45
 
6.0%
k 28
 
3.7%
m 28
 
3.7%
c 27
 
3.6%
Other values (23) 255
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 552
73.7%
Uppercase Letter 125
 
16.7%
Other Punctuation 53
 
7.1%
Connector Punctuation 19
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 83
15.0%
e 64
11.6%
r 57
10.3%
o 55
10.0%
i 54
9.8%
k 28
 
5.1%
m 28
 
5.1%
c 27
 
4.9%
s 24
 
4.3%
h 24
 
4.3%
Other values (10) 108
19.6%
Uppercase Letter
ValueCountFrequency (%)
A 45
36.0%
Y 19
15.2%
N 19
15.2%
S 12
 
9.6%
E 9
 
7.2%
T 6
 
4.8%
K 5
 
4.0%
C 5
 
4.0%
L 2
 
1.6%
P 2
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/ 53
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 677
90.4%
Common 72
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 83
 
12.3%
e 64
 
9.5%
r 57
 
8.4%
o 55
 
8.1%
i 54
 
8.0%
A 45
 
6.6%
k 28
 
4.1%
m 28
 
4.1%
c 27
 
4.0%
s 24
 
3.5%
Other values (21) 212
31.3%
Common
ValueCountFrequency (%)
/ 53
73.6%
_ 19
 
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 83
 
11.1%
e 64
 
8.5%
r 57
 
7.6%
o 55
 
7.3%
i 54
 
7.2%
/ 53
 
7.1%
A 45
 
6.0%
k 28
 
3.7%
m 28
 
3.7%
c 27
 
3.6%
Other values (23) 255
34.0%

network.officialSite
Text

Missing 

Distinct15
Distinct (%)83.3%
Missing707
Missing (%)97.5%
Memory size5.8 KiB
2025-03-30T09:27:31.961911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length30
Mean length22.833333
Min length15

Characters and Unicode

Total characters411
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)66.7%

Sample

1st rowhttps://www.cbc.ca/
2nd rowhttps://www.nbc.com/
3rd rowhttps://abc.com/
4th rowhttps://tv.nova.cz/
5th rowhttps://www.cwtv.com/
ValueCountFrequency (%)
https://www.nbc.com 2
 
11.1%
https://www.cbs.com 2
 
11.1%
https://www.bbc.co.uk/bbcone 2
 
11.1%
https://tv.nova.cz 1
 
5.6%
https://www.cwtv.com 1
 
5.6%
https://www.cbc.ca 1
 
5.6%
https://abc.com 1
 
5.6%
https://tv3.ru 1
 
5.6%
https://www.foxnews.com 1
 
5.6%
https://www.tbn.org 1
 
5.6%
Other values (5) 5
27.8%
2025-03-30T09:27:32.206513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 55
13.4%
t 43
10.5%
w 43
10.5%
. 36
 
8.8%
c 29
 
7.1%
s 26
 
6.3%
h 20
 
4.9%
p 19
 
4.6%
o 19
 
4.6%
: 18
 
4.4%
Other values (20) 103
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 296
72.0%
Other Punctuation 109
 
26.5%
Dash Punctuation 4
 
1.0%
Decimal Number 2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 43
14.5%
w 43
14.5%
c 29
9.8%
s 26
8.8%
h 20
 
6.8%
p 19
 
6.4%
o 19
 
6.4%
b 17
 
5.7%
n 14
 
4.7%
a 12
 
4.1%
Other values (14) 54
18.2%
Other Punctuation
ValueCountFrequency (%)
/ 55
50.5%
. 36
33.0%
: 18
 
16.5%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 296
72.0%
Common 115
 
28.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 43
14.5%
w 43
14.5%
c 29
9.8%
s 26
8.8%
h 20
 
6.8%
p 19
 
6.4%
o 19
 
6.4%
b 17
 
5.7%
n 14
 
4.7%
a 12
 
4.1%
Other values (14) 54
18.2%
Common
ValueCountFrequency (%)
/ 55
47.8%
. 36
31.3%
: 18
 
15.7%
- 4
 
3.5%
3 1
 
0.9%
5 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 55
13.4%
t 43
10.5%
w 43
10.5%
. 36
 
8.8%
c 29
 
7.1%
s 26
 
6.3%
h 20
 
4.9%
p 19
 
4.6%
o 19
 
4.6%
: 18
 
4.4%
Other values (20) 103
25.1%

webChannel
Unsupported

Missing  Rejected  Unsupported 

Missing725
Missing (%)100.0%
Memory size5.8 KiB

webChannel.country
Unsupported

Missing  Rejected  Unsupported 

Missing725
Missing (%)100.0%
Memory size5.8 KiB

dvdCountry.name
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing721
Missing (%)99.4%
Memory size5.8 KiB
2025-03-30T09:27:32.303679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length15.5
Mean length11.25
Min length7

Characters and Unicode

Total characters45
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowUkraine
2nd rowUnited States
3rd rowRussian Federation
4th rowUkraine
ValueCountFrequency (%)
ukraine 2
33.3%
united 1
16.7%
states 1
16.7%
russian 1
16.7%
federation 1
16.7%
2025-03-30T09:27:32.481714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6
13.3%
a 5
11.1%
i 5
11.1%
n 5
11.1%
t 4
8.9%
U 3
6.7%
r 3
6.7%
s 3
6.7%
k 2
 
4.4%
d 2
 
4.4%
Other values (6) 7
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37
82.2%
Uppercase Letter 6
 
13.3%
Space Separator 2
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6
16.2%
a 5
13.5%
i 5
13.5%
n 5
13.5%
t 4
10.8%
r 3
8.1%
s 3
8.1%
k 2
 
5.4%
d 2
 
5.4%
u 1
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
U 3
50.0%
S 1
 
16.7%
R 1
 
16.7%
F 1
 
16.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43
95.6%
Common 2
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6
14.0%
a 5
11.6%
i 5
11.6%
n 5
11.6%
t 4
9.3%
U 3
7.0%
r 3
7.0%
s 3
7.0%
k 2
 
4.7%
d 2
 
4.7%
Other values (5) 5
11.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6
13.3%
a 5
11.1%
i 5
11.1%
n 5
11.1%
t 4
8.9%
U 3
6.7%
r 3
6.7%
s 3
6.7%
k 2
 
4.4%
d 2
 
4.4%
Other values (6) 7
15.6%

dvdCountry.code
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing721
Missing (%)99.4%
Memory size5.8 KiB
2025-03-30T09:27:32.542308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowUA
2nd rowUS
3rd rowRU
4th rowUA
ValueCountFrequency (%)
ua 2
50.0%
us 1
25.0%
ru 1
25.0%
2025-03-30T09:27:32.682057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4
50.0%
A 2
25.0%
S 1
 
12.5%
R 1
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 4
50.0%
A 2
25.0%
S 1
 
12.5%
R 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 8
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 4
50.0%
A 2
25.0%
S 1
 
12.5%
R 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 4
50.0%
A 2
25.0%
S 1
 
12.5%
R 1
 
12.5%

dvdCountry.timezone
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing721
Missing (%)99.4%
Memory size5.8 KiB
2025-03-30T09:27:32.773318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13
Min length11

Characters and Unicode

Total characters52
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowEurope/Kyiv
2nd rowAmerica/New_York
3rd rowAsia/Kamchatka
4th rowEurope/Kyiv
ValueCountFrequency (%)
europe/kyiv 2
50.0%
america/new_york 1
25.0%
asia/kamchatka 1
25.0%
2025-03-30T09:27:32.954942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5
 
9.6%
e 4
 
7.7%
r 4
 
7.7%
i 4
 
7.7%
/ 4
 
7.7%
o 3
 
5.8%
K 3
 
5.8%
p 2
 
3.8%
u 2
 
3.8%
E 2
 
3.8%
Other values (13) 19
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38
73.1%
Uppercase Letter 9
 
17.3%
Other Punctuation 4
 
7.7%
Connector Punctuation 1
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5
13.2%
e 4
10.5%
r 4
10.5%
i 4
10.5%
o 3
 
7.9%
p 2
 
5.3%
u 2
 
5.3%
y 2
 
5.3%
v 2
 
5.3%
m 2
 
5.3%
Other values (6) 8
21.1%
Uppercase Letter
ValueCountFrequency (%)
K 3
33.3%
E 2
22.2%
A 2
22.2%
N 1
 
11.1%
Y 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47
90.4%
Common 5
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5
 
10.6%
e 4
 
8.5%
r 4
 
8.5%
i 4
 
8.5%
o 3
 
6.4%
K 3
 
6.4%
p 2
 
4.3%
u 2
 
4.3%
E 2
 
4.3%
y 2
 
4.3%
Other values (11) 16
34.0%
Common
ValueCountFrequency (%)
/ 4
80.0%
_ 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5
 
9.6%
e 4
 
7.7%
r 4
 
7.7%
i 4
 
7.7%
/ 4
 
7.7%
o 3
 
5.8%
K 3
 
5.8%
p 2
 
3.8%
u 2
 
3.8%
E 2
 
3.8%
Other values (13) 19
36.5%

Interactions

2025-03-30T09:27:16.254023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:05.881512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.164168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.395011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.710842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.048989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.465536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.591081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.505597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.284738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.353530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:05.996170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.294621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.533881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.853629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.181921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.574140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.690792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.589270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.381641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.445915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.116167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.394562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.646886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.981524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.340843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.671647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.769969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.658659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.469433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.527357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.243777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.517743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.776716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.127236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.480381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.766758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.855712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.739364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.570678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.603082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.364358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.644413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.908905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.245642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.628643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.841153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.958688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.814545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.668990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.703393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.502928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.762672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.046360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.375491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.757140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.923996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.055815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.895295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.770731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.787080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.626714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.946277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.176511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.506887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:11.898349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.009113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.139464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.983002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.881764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.871919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.752810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.059853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.330489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.646363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.032147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.093774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.225947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.056562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.986300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.952736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:06.864039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.153967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.443638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.759643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.175979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.445485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.313234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.123249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.063760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:17.053092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:07.004825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:08.261686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:09.591911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:10.916020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:12.310343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:13.521159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:14.413400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:15.198320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-30T09:27:16.162559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-30T09:27:33.062033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
averageRuntimeexternals.thetvdbexternals.tvrageidlanguagenetwork.country.codenetwork.country.namenetwork.country.timezonenetwork.idrating.averageruntimestatustypeupdatedwebChannel.country.codewebChannel.country.namewebChannel.country.timezonewebChannel.idweight
averageRuntime1.000-0.0970.056-0.0790.0000.3950.3950.395-0.2540.0090.9940.0920.2870.0190.1260.1260.1260.1690.142
externals.thetvdb-0.0971.0000.9360.8110.1010.3810.3810.3810.126-0.235-0.1510.2240.191-0.3940.1250.1250.1250.053-0.401
externals.tvrage0.0560.9361.0000.2140.4931.0001.0001.000-1.000-0.179-0.2110.0000.316-0.0320.4240.4240.4240.292-0.210
id-0.0790.8110.2141.0000.1050.0000.0000.0000.257-0.2150.0000.2030.172-0.2310.1860.1860.1860.196-0.549
language0.0000.1010.4930.1051.0000.9440.9440.9440.4070.0000.1420.3650.2130.1620.8900.8900.8900.4150.140
network.country.code0.3950.3811.0000.0000.9441.0001.0001.0000.2880.0000.2590.5830.2670.2371.0001.0001.0000.3320.220
network.country.name0.3950.3811.0000.0000.9441.0001.0001.0000.2880.0000.2590.5830.2670.2371.0001.0001.0000.3320.220
network.country.timezone0.3950.3811.0000.0000.9441.0001.0001.0000.2880.0000.2590.5830.2670.2371.0001.0001.0000.3320.220
network.id-0.2540.126-1.0000.2570.4070.2880.2880.2881.0000.505-0.2050.0000.000-0.4320.4410.4410.4410.041-0.449
rating.average0.009-0.235-0.179-0.2150.0000.0000.0000.0000.5051.0000.0050.1880.1000.2760.0400.0400.0400.0040.164
runtime0.994-0.151-0.2110.0000.1420.2590.2590.259-0.2050.0051.0000.0000.2990.0720.0000.0000.0000.3920.050
status0.0920.2240.0000.2030.3650.5830.5830.5830.0000.1880.0001.0000.3680.3180.4480.4480.4480.1570.163
type0.2870.1910.3160.1720.2130.2670.2670.2670.0000.1000.2990.3681.0000.1340.2710.2710.2710.2260.098
updated0.019-0.394-0.032-0.2310.1620.2370.2370.237-0.4320.2760.0720.3180.1341.0000.1470.1470.1470.0360.301
webChannel.country.code0.1260.1250.4240.1860.8901.0001.0001.0000.4410.0400.0000.4480.2710.1471.0001.0001.0000.5500.147
webChannel.country.name0.1260.1250.4240.1860.8901.0001.0001.0000.4410.0400.0000.4480.2710.1471.0001.0001.0000.5500.147
webChannel.country.timezone0.1260.1250.4240.1860.8901.0001.0001.0000.4410.0400.0000.4480.2710.1471.0001.0001.0000.5500.147
webChannel.id0.1690.0530.2920.1960.4150.3320.3320.3320.0410.0040.3920.1570.2260.0360.5500.5500.5501.000-0.141
weight0.142-0.401-0.210-0.5490.1400.2200.2200.220-0.4490.1640.0500.1630.0980.3010.1470.1470.147-0.1411.000

Missing values

2025-03-30T09:27:17.275911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T09:27:17.617133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-30T09:27:18.338084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idurlnametypelanguagegenresstatusruntimeaverageRuntimepremieredendedofficialSiteweightnetworkdvdCountrysummaryupdatedschedule.timeschedule.daysrating.averagewebChannel.idwebChannel.namewebChannel.country.namewebChannel.country.codewebChannel.country.timezonewebChannel.officialSiteexternals.tvrageexternals.thetvdbexternals.imdbimage.mediumimage.original_links.self.href_links.previousepisode.href_links.previousepisode.nameimage_links.nextepisode.href_links.nextepisode.namenetwork.idnetwork.namenetwork.country.namenetwork.country.codenetwork.country.timezonenetwork.officialSitewebChannelwebChannel.countrydvdCountry.namedvdCountry.codedvdCountry.timezone
051908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussian[Drama, Comedy, Romance]EndedNaN19.02020-11-122024-01-01https://www.ivi.ru/watch/nezhnost40NaNNaNNone1704215354[]NaN337.0ИвиRussian FederationRUAsia/Kamchatkahttps://www.ivi.ru/NaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/282/707449.jpghttps://static.tvmaze.com/uploads/images/original_untouched/282/707449.jpghttps://api.tvmaze.com/shows/51908https://api.tvmaze.com/episodes/2730595Серия 10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
159205https://www.tvmaze.com/shows/59205/predposlednaa-instanciaПредпоследняя инстанцияScriptedRussian[Drama, Comedy, Supernatural]EndedNaN26.02022-01-012024-01-08https://okko.tv/serial/predposlednjaja-instancija58NaNNaNNone1704783571[Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday]NaN366.0OkkoRussian FederationRUAsia/Kamchatkahttps://okko.tv/NaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/388/970660.jpghttps://static.tvmaze.com/uploads/images/original_untouched/388/970660.jpghttps://api.tvmaze.com/shows/59205https://api.tvmaze.com/episodes/2719129Серия 30NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
259484https://www.tvmaze.com/shows/59484/manunaМанюняScriptedRussian[Comedy, Adventure, Children]RunningNaN20.02021-12-15Nonehttps://okko.tv/serial/manjunja32NaNNaNNone1703404987[Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday]6.3366.0OkkoRussian FederationRUAsia/Kamchatkahttps://okko.tv/NaN413379.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/383/957782.jpghttps://static.tvmaze.com/uploads/images/original_untouched/383/957782.jpghttps://api.tvmaze.com/shows/59484https://api.tvmaze.com/episodes/2718223Серия 10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
372871https://www.tvmaze.com/shows/72871/nedetskoe-kinoНедетское киноScriptedNone[Comedy, Fantasy]RunningNaNNaN2024-01-01Nonehttps://wink.ru/series/ne-detskoe-kino-year-2023?ysclid=lpbaiai0cw6547635983NaNNaNNone1704019326[]NaN541.0WinkRussian FederationRUAsia/KamchatkaNoneNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/488/1221089.jpghttps://static.tvmaze.com/uploads/images/original_untouched/488/1221089.jpghttps://api.tvmaze.com/shows/72871https://api.tvmaze.com/episodes/2692657Серия 12NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
473221https://www.tvmaze.com/shows/73221/uspesnyjУспешныйScriptedRussian[Comedy]RunningNaNNaN2024-01-01Nonehttps://kion.ru/video/serial/822094895/season/822095042/episode/8220947157NaNNaN<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>1735808422[]NaN423.0KIONRussian FederationRUAsia/Kamchatkahttps://kion.ru/NaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/496/1241006.jpghttps://static.tvmaze.com/uploads/images/original_untouched/496/1241006.jpghttps://api.tvmaze.com/shows/73221https://api.tvmaze.com/episodes/3098220Серия 10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
573590https://www.tvmaze.com/shows/73590/kak-druza-zahara-zeniliКак друзья Захара женилиScriptedNone[Comedy]EndedNaN26.02024-01-012024-02-06None19NaNNaNNone1707351592[]NaN366.0OkkoRussian FederationRUAsia/Kamchatkahttps://okko.tv/NaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/495/1237905.jpghttps://static.tvmaze.com/uploads/images/original_untouched/495/1237905.jpghttps://api.tvmaze.com/shows/73590https://api.tvmaze.com/episodes/2730985Серия 17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
673893https://www.tvmaze.com/shows/73893/imperatricyИмператрицыDocumentaryNone[Drama, History]EndedNaN39.02024-01-012024-01-01https://premier.one/show/imperatritsy-mini-serial4NaNNaNNone1705416247[]NaN281.0PremierRussian FederationRUAsia/Kamchatkahttps://premier.one/NaNNaNNoneNaNNaNhttps://api.tvmaze.com/shows/73893https://api.tvmaze.com/episodes/2743354ТриумфNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
773780https://www.tvmaze.com/shows/73780/planet-lulinPlanet LulinScriptedEnglish[Children, Fantasy, Science-Fiction]To Be DeterminedNaN22.02024-01-01Nonehttps://iview.abc.net.au/show/planet-lulin31NaNNaN<p>Lulin wasn't expecting to develop alien powers, or intergalactic invaders to crash her school science comp, but hey, Year 6 is full of surprises!</p>1705107854[]NaN149.0ABC iviewAustraliaAUAustralia/Sydneyhttps://iview.abc.net.au/NaN444536.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/498/1246029.jpghttps://static.tvmaze.com/uploads/images/original_untouched/498/1246029.jpghttps://api.tvmaze.com/shows/73780https://api.tvmaze.com/episodes/2732803The VoidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
855019https://www.tvmaze.com/shows/55019/supreme-god-emperorSupreme God EmperorAnimationChinese[Adventure, Anime, Fantasy]RunningNaN10.02020-05-18Nonehttps://v.qq.com/x/search/?q=无上神帝&stag=&smartbox_ab=80NaNNaN<p>Ten thousand years ago, Muyun's fairy King was secretly accounted for by holding a Zhuxian figure, and after a long sleep, he awakened in the famous "Muyun waste" of the southern Yun Empire in the Land of Heaven. When Muyun first woke up, he was deliberately bothered by the student Miaoxianyu. Muyun easily completed the Miaoxianyu trap, and he gave more and more alchemy skills by analogy, so the Alchemy masters outside the door could not ask for appreciation. Endless back home, Mu Yun learns that he is about to marry Nona Qin Qin Mengyao. Qin Mengyao was cold and toxic, but could not live until he was 20 years old. The marriage was only for the sake of pastoralists and family of Qin. However, under Mu Linchen's enticement, Mu Yun approves the family's issue on the condition of alchemy.</p><p><br /> </p>173937418510:00[Monday, Friday]NaN104.0Tencent QQChinaCNAsia/Shanghaihttps://v.qq.com/NaN388383.0tt20603062https://static.tvmaze.com/uploads/images/medium_portrait/311/778540.jpghttps://static.tvmaze.com/uploads/images/original_untouched/311/778540.jpghttps://api.tvmaze.com/shows/55019https://api.tvmaze.com/episodes/3145310Episode 469NaNhttps://api.tvmaze.com/episodes/3145311Episode 470NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
962143https://www.tvmaze.com/shows/62143/against-the-sky-supremeAgainst the Sky SupremeAnimationChinese[Action, Adventure, Anime, Fantasy]RunningNaN7.02021-07-09Nonehttps://v.qq.com/x/cover/mzc00200azkttu2.html37NaNNaN<p>The former mighty Gods of the heavens, after ten thousand lifetimes of reincarnation tragically destroyed! The cruel curse, the hatred of ten thousand lives, Tan Yun determined, no longer sink! The most important thing is that you have to be able to get to the top of the world, step by step, stepping on the corpses of your enemies! To kill against the sky, across all the worlds, only I am the supreme!</p>173861911510:00[Monday, Friday]7.8104.0Tencent QQChinaCNAsia/Shanghaihttps://v.qq.com/NaN406729.0tt15816496https://static.tvmaze.com/uploads/images/medium_portrait/409/1023562.jpghttps://static.tvmaze.com/uploads/images/original_untouched/409/1023562.jpghttps://api.tvmaze.com/shows/62143https://api.tvmaze.com/episodes/3135344Episode 396NaNhttps://api.tvmaze.com/episodes/3135345Episode 397NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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71534499https://www.tvmaze.com/shows/34499/off-gun-fun-nightOff Gun Fun NightTalk ShowThai[Drama, Comedy]EndedNaN23.02017-11-122024-01-31https://tv.line.me/offgunfunnight?lang=th_TH34NaNNaN<p>A monthly talk show where Off and Gun host a special guest and a lot of fun and laughs ensue. Airs every 12th of the month.</p>1706727249[]NaN88.0LINE TVNaNNaNNaNhttps://www.linetv.tw/NaN347036.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/143/357788.jpghttps://static.tvmaze.com/uploads/images/original_untouched/143/357788.jpghttps://api.tvmaze.com/shows/34499https://api.tvmaze.com/episodes/2730513OffGun Fun Night: Special with Tay, New, Junior, and MarkNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71640868https://www.tvmaze.com/shows/40868/gods-school-the-olympian-godsGods' School: The Olympian GodsAnimationEnglish[]Running15.013.02019-01-31Nonehttps://www.youtube.com/channel/UC4E060sRvClagErOdYZZjVA37NaNNaN<p>Mount Olympus is the divine sanctuary created by the Titans for the young gods and goddesses, Among the young immortals, one young goddess, Eris, is a black sheep. She has an horrible reputation, she doesn't fit the high standards of Mount Olympus and she is is being avoid like the plague. But her meeting with a mortal is going to change her divine destiny.</p>1706859211[Saturday]NaN21.0YouTubeNaNNaNNaNhttps://www.youtube.comNaN355452.0tt8929826https://static.tvmaze.com/uploads/images/medium_portrait/182/456799.jpghttps://static.tvmaze.com/uploads/images/original_untouched/182/456799.jpghttps://api.tvmaze.com/shows/40868https://api.tvmaze.com/episodes/2761121Episode 8 - Hera and ZeusNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71753613https://www.tvmaze.com/shows/53613/marvel-studios-assembledMarvel Studios: AssembledDocumentaryEnglish[]RunningNaN57.02021-03-12Nonehttps://disneyplus.com/series/marvel-studios-assembled/3RUQKboZV3FF91NaNNaN<p><b>Marvel Studios: Assembled</b> is a comprehensive documentary series streaming on Disney+ that chronicles the creation of Marvel Studios' thrilling new shows and theatrical releases. Journey behind-the-scenes of productions such as <i>WandaVision</i>, <i>The Falcon and the Winter Soldier</i>, and <i>Loki</i> via exclusive on-set footage. <i>Assembled</i> is an immersive, and in-depth examination of the next phase of the Marvel Cinematic Universe.</p>1740065413[]7.9287.0Disney+NaNNaNNaNhttps://www.disneyplus.com/NaN396948.0tt14094206https://static.tvmaze.com/uploads/images/medium_portrait/297/742558.jpghttps://static.tvmaze.com/uploads/images/original_untouched/297/742558.jpghttps://api.tvmaze.com/shows/53613https://api.tvmaze.com/episodes/3081475The Making of Agatha All Along'NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71866629https://www.tvmaze.com/shows/66629/love-never-lies-polskaLove Never Lies: PolskaRealityPolish[Romance]To Be DeterminedNaN51.02023-01-25Nonehttps://www.netflix.com/title/8158270647NaNNaN<p>Six couples test their trust with an eye-scanning lie detector in this reality series where deception costs money, and the truth comes with a prize.</p>1741648633[]NaN1.0NetflixNaNNaNNaNhttps://www.netflix.com/NaN430211.0tt25377596https://static.tvmaze.com/uploads/images/medium_portrait/443/1109090.jpghttps://static.tvmaze.com/uploads/images/original_untouched/443/1109090.jpghttps://api.tvmaze.com/shows/66629https://api.tvmaze.com/episodes/3160261The ReunionNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71973378https://www.tvmaze.com/shows/73378/choirChoirDocumentaryEnglish[Family, Music]RunningNaN46.02024-01-31Nonehttps://www.disneyplus.com/series/choir/2ZObNbv5pKlj54NaNNaN<p><b>Choir</b> follows the kids of the Detroit Youth Choir as they prepare for the performance of a lifetime. Through their eyes, we experience the highs and lows of life growing up in Detroit, navigating the challenges of balancing family, school, and athletics, all while pursuing their dreams of taking their talents to the next level and performing on one of the world's biggest stages. Following their 2019 appearance on America's Got Talent, it's a pivotal time for the choir and its director, Anthony White, as he's faced with the combined challenges of replacing several key members, keeping the choir relevant in Detroit, and finding the next big opportunity that will put them back in the national spotlight.</p>1706780799[]NaN287.0Disney+NaNNaNNaNhttps://www.disneyplus.com/NaN443679.0tt28080721https://static.tvmaze.com/uploads/images/medium_portrait/500/1251455.jpghttps://static.tvmaze.com/uploads/images/original_untouched/500/1251455.jpghttps://api.tvmaze.com/shows/73378https://api.tvmaze.com/episodes/2711948Primetime ReadyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72073413https://www.tvmaze.com/shows/73413/baby-banditoBaby BanditoScriptedSpanish[Drama, Crime, Thriller]EndedNaN38.02024-01-312024-01-31https://www.netflix.com/title/8161919842NaNNaN<p>After skater Kevin and his crew pull off Chile's biggest heist, reckless love –and social media– threatens everyone's fortunes. Inspired by true events.</p>1706822844[]5.31.0NetflixNaNNaNNaNhttps://www.netflix.com/NaN444012.0tt27997713https://static.tvmaze.com/uploads/images/medium_portrait/492/1232077.jpghttps://static.tvmaze.com/uploads/images/original_untouched/492/1232077.jpghttps://api.tvmaze.com/shows/73413https://api.tvmaze.com/episodes/2759361Más vale tener amigos que plataNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72173714https://www.tvmaze.com/shows/73714/alexander-the-making-of-a-godAlexander: The Making of a GodDocumentaryEnglish[History]EndedNaN39.02024-01-312024-01-31https://www.netflix.com/title/8132519488NaNNaN<p>Combining expert interviews with gripping reenactments, this docudrama explores the life of Alexander the Great through his conquest of the Persian Empire.</p>1707151653[]6.11.0NetflixNaNNaNNaNhttps://www.netflix.com/NaN444188.0tt27494999https://static.tvmaze.com/uploads/images/medium_portrait/502/1255464.jpghttps://static.tvmaze.com/uploads/images/original_untouched/502/1255464.jpghttps://api.tvmaze.com/shows/73714https://api.tvmaze.com/episodes/2729748A World Still to ConquerNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72274390https://www.tvmaze.com/shows/74390/fighting-for-loveFighting for LoveScriptedChinese[Action, Romance, War]EndedNaN45.02024-01-312024-02-21https://www.iqiyi.com/v_26mc92p2eag.html13NaNNaN<p>The story follows Amai, a female general of the founding era of the Southern Xia dynasty, who discards her feminine attire and dons battle armor, enduring hardships to become a legendary female warrior. Amai (Zhang Tianai), the daughter of the Duke of Jingguo in Southern Xia, witnesses her entire family being killed by her childhood friend Chen Qi. Years later, Amai, now a young girl, disguises herself as a man and roams the martial world, seeking revenge against Chen Qi. Through a series of coincidences, Amai saves Shang Yizhi (Zhang Haowei), the son of the Grand Princess, and subsequently helps him escape from dangerous situations multiple times, intertwining their destinies. As war breaks out, Amai sheds her feminine attire and joins the military, becoming an infantry soldier. With her exceptional military talents, she achieves remarkable feats and rises to the rank of General Mai. Alongside this, she assists Shang Yizhi, who is hunted and faces difficulties, in finding his true self and accomplishing great deeds. On the battlefield, Amai repeatedly clashes with General Chang Yuqing (Wang Ruichang) from the enemy forces. Despite their confrontations, they unintentionally go through life-and-death situations together, developing a mutual understanding and respect. However, faced with the brutality of war, Amai willingly sets aside personal attachments and uses her youth and fervor to defend her army. Eventually, after the war's victory, Amai leaves the military, returns to her female identity, retreats from the world, and ultimately finds her own happiness.</p>1708493403[Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday]NaN67.0iQIYINaNNaNNaNhttps://www.iq.com/NaN439696.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/502/1255381.jpghttps://static.tvmaze.com/uploads/images/original_untouched/502/1255381.jpghttps://api.tvmaze.com/shows/74390https://api.tvmaze.com/episodes/2759394Episode 36NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72378088https://www.tvmaze.com/shows/78088/hot-ones-versusHot Ones VersusTalk ShowEnglish[Food]RunningNaN15.02024-01-31Nonehttps://www.youtube.com/playlist?list=PLAzrgbu8gEMKThlhJv0ftFDvsumn0f-HN16NaNNaN<p>"Hot Ones" spin-off series where guests have two choices: Tell the truth, or suffer the wrath of the Last Dab. Whoever eats the most wings, loses.</p>1742525809[]NaN21.0YouTubeNaNNaNNaNhttps://www.youtube.comNaNNaNtt31323246https://static.tvmaze.com/uploads/images/medium_portrait/525/1313265.jpghttps://static.tvmaze.com/uploads/images/original_untouched/525/1313265.jpghttps://api.tvmaze.com/shows/78088https://api.tvmaze.com/episodes/3179559Asif Ali vs. Poorna Jagannathan vs. Saagar ShaikhNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72475220https://www.tvmaze.com/shows/75220/sues-placesSue's PlacesTalk ShowEnglish[Sports]RunningNaNNaN2024-01-31Nonehttps://www.espn.com/watch/series/0b15b405-50b7-4e8b-a553-cfee4ef2a89d/sues-places9NaNNaN<p>UConn legend Sue Bird explores the rich history, traditions and seminal moments of men's and women's college basketball.</p>1710010766[Wednesday]NaN265.0ESPN+United StatesUSAmerica/New_YorkNoneNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/507/1269695.jpghttps://static.tvmaze.com/uploads/images/original_untouched/507/1269695.jpghttps://api.tvmaze.com/shows/75220https://api.tvmaze.com/episodes/2792942Oop DreamsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN